Body normal network light and rendering control

ABSTRACT

Methods and systems are disclosed for performing operations for applying augmented reality elements to a fashion item. The operations include receiving an image that includes a depiction of a person wearing a fashion item. The operations include generating a segmentation of the fashion item worn by the person depicted in the image. The operations include extracting a portion of the image corresponding to the segmentation of the fashion item; estimating an angle of each pixel in the portion of the image relative to a camera used to capture the image. The operations include applying one or more augmented reality elements to the fashion item in the image based on the estimated angle of each pixel in the portion of the image relative to the camera used to capture the image.

TECHNICAL FIELD

The present disclosure relates generally to providing augmented realityexperiences using a messaging application.

BACKGROUND

Augmented Reality (AR) is a modification of a virtual environment. Forexample, in Virtual Reality (VR), a user is completely immersed in avirtual world, whereas in AR, the user is immersed in a world wherevirtual objects are combined or superimposed on the real world. An ARsystem aims to generate and present virtual objects that interactrealistically with a real-world environment and with each other.Examples of AR applications can include single or multiple player videogames, instant messaging systems, and the like.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

In the drawings, which are not necessarily drawn to scale, like numeralsmay describe similar components in different views. To easily identifythe discussion of any particular element or act, the most significantdigit or digits in a reference number refer to the figure number inwhich that element is first introduced. Some nonlimiting examples areillustrated in the figures of the accompanying drawings in which:

FIG. 1 is a diagrammatic representation of a networked environment inwhich the present disclosure may be deployed, in accordance with someexamples.

FIG. 2 is a diagrammatic representation of a messaging clientapplication, in accordance with some examples.

FIG. 3 is a diagrammatic representation of a data structure asmaintained in a database, in accordance with some examples.

FIG. 4 is a diagrammatic representation of a message, in accordance withsome examples.

FIG. 5 is a block diagram showing an example AR fashion control system,according to example examples.

FIGS. 6, 7, and 8 are diagrammatic representations of outputs of the ARfashion control system, in accordance with some examples.

FIG. 9 is a flowchart illustrating example operations of the AR fashioncontrol system, according to some examples.

FIG. 10 is a diagrammatic representation of a machine in the form of acomputer system within which a set of instructions may be executed forcausing the machine to perform any one or more of the methodologiesdiscussed herein, in accordance with some examples.

FIG. 11 is a block diagram showing a software architecture within whichexamples may be implemented.

DETAILED DESCRIPTION

The description that follows includes systems, methods, techniques,instruction sequences, and computing machine program products thatembody illustrative examples of the disclosure. In the followingdescription, for the purposes of explanation, numerous specific detailsare set forth in order to provide an understanding of various examples.It will be evident, however, to those skilled in the art, that examplesmay be practiced without these specific details. In general, well-knowninstruction instances, protocols, structures, and techniques are notnecessarily shown in detail.

Typically, VR and (AR) systems display images representing a given userby capturing an image of the user and, in addition, obtaining a depthmap using a depth sensor of the real-world human body depicted in theimage. By processing the depth map and the image together, the VR and ARsystems can detect positioning of a user in the image and canappropriately modify the user or background in the images. While suchsystems work well, the need for a depth sensor limits the scope of theirapplications. This is because adding depth sensors to user devices forthe purpose of modifying images increases the overall cost andcomplexity of the devices, making them less attractive.

Certain systems do away with the need to use depth sensors to modifyimages. For example, certain systems allow users to replace a backgroundin a videoconference in which a face of the user is detected.Specifically, such systems can use specialized techniques that areoptimized for recognizing a face of a user to identify the background inthe images that depict the user's face. These systems can then replaceonly those pixels that depict the background so that the real-worldbackground is replaced with an alternate background in the images. Suchsystems though are generally incapable of recognizing a whole body of auser. As such, if the user is more than a threshold distance from thecamera such that more than just the face of the user is captured by thecamera, the replacement of the background with an alternate backgroundbegins to fail. In such cases, the image quality is severely impacted,and portions of the face and body of the user can be inadvertentlyremoved by the system as the system falsely identifies such portions asbelonging to the background rather than the foreground of the images.Also, such systems fail to properly replace the background when morethan one user is depicted in the image or video feed. Because suchsystems are generally incapable of distinguishing a whole body of a userin an image from a background, these systems are also unable to applyvisual effects to certain portions of a user's body, such as articles ofclothing.

The disclosed techniques improve the efficiency of using the electronicdevice by segmenting articles of clothing, fashion items, or garmentsworn by a user depicted in an image or video, such as a shirt worn bythe user depicted in the image, in addition to creating a whole-bodymodel of the user depicted in the image or video. By segmenting thearticles of clothing, fashion items, or garments worn by a user or wornby different respective users depicted in an image and estimating pixelangles of the articles of clothing, fashion items, or garments relativeto a camera or surface normal of the camera, the disclosed techniquescan apply one or more visual effects to the image or video, such as oneor more AR elements. In an example, the disclosed techniques can changelighting effects and reflections on articles of clothing, fashion items,or garments and can re-focus artificial or AR light being applied tosuch articles of clothing, fashion items, or garments from differentdirections. In another example, the disclosed techniques can change thematerials of articles of clothing, fashion items, or garments so thatlight is being reflected and/or absorbed in different ways based on thedirection and orientation of such articles of clothing, fashion items,or garments relative to the camera or surface normal of the camera.

In an example, the disclosed techniques apply a machine learningtechnique to generate a segmentation of a shirt (or upper garment) wornby a user depicted in an image (e.g., to distinguish pixelscorresponding to the shirt or multiple garments worn by the user frompixels corresponding to a background of the image or a user's bodyparts). In this way, the disclosed techniques can apply one or morevisual effects to the shirt worn by a user that has been segmented inthe current image. Also, by generating the segmentation of the shirt, aposition/location of the shirt in a video feed can be trackedindependently or separately from positions of a user's body parts, suchas a hand. As a result, a realistic display is provided that shows theuser wearing a shirt (or upper garment) while also presenting ARelements on the shirt in a way that is intuitive for the user tointeract with and select. As used herein, “article of clothing,”“fashion item,” and “garment” are used interchangeably and should beunderstood to have the same meaning. This improves the overallexperience of the user in using the electronic device. Also, byperforming such segmentations without using a depth sensor, the overallamount of system resources needed to accomplish a task is reduced.

Networked Computing Environment

FIG. 1 is a block diagram showing an example messaging system 100 forexchanging data (e.g., messages and associated content) over a network.The messaging system 100 includes multiple instances of a client device102, each of which hosts a number of applications, including a messagingclient 104 and other external applications 109 (e.g., third-partyapplications). Each messaging client 104 is communicatively coupled toother instances of the messaging client 104 (e.g., hosted on respectiveother client devices 102), a messaging server system 108 and externalapp(s) servers 110 via a network 112 (e.g., the Internet). A messagingclient 104 can also communicate with locally-hosted third-partyapplications, such as external apps 109 using Application ProgrammingInterfaces (APIs).

A messaging client 104 is able to communicate and exchange data withother messaging clients 104 and with the messaging server system 108 viathe network 112. The data exchanged between messaging clients 104, andbetween a messaging client 104 and the messaging server system 108,includes functions (e.g., commands to invoke functions) as well aspayload data (e.g., text, audio, video or other multimedia data).

The messaging server system 108 provides server-side functionality viathe network 112 to a particular messaging client 104. While certainfunctions of the messaging system 100 are described herein as beingperformed by either a messaging client 104 or by the messaging serversystem 108, the location of certain functionality either within themessaging client 104 or the messaging server system 108 may be a designchoice. For example, it may be technically preferable to initiallydeploy certain technology and functionality within the messaging serversystem 108 but to later migrate this technology and functionality to themessaging client 104 where a client device 102 has sufficient processingcapacity.

The messaging server system 108 supports various services and operationsthat are provided to the messaging client 104. Such operations includetransmitting data to, receiving data from, and processing data generatedby the messaging client 104. This data may include message content,client device information, geolocation information, media augmentationand overlays, message content persistence conditions, social networkinformation, and live event information, as examples. Data exchangeswithin the messaging system 100 are invoked and controlled throughfunctions available via user interfaces of the messaging client 104.

Turning now specifically to the messaging server system 108, an APIserver 116 is coupled to, and provides a programmatic interface to,application servers 114. The application servers 114 are communicativelycoupled to a database server 120, which facilitates access to a database126 that stores data associated with messages processed by theapplication servers 114. Similarly, a web server 128 is coupled to theapplication servers 114 and provides web-based interfaces to theapplication servers 114. To this end, the web server 128 processesincoming network requests over the Hypertext Transfer Protocol (HTTP)and several other related protocols.

The API server 116 receives and transmits message data (e.g., commandsand message payloads) between the client device 102 and the applicationservers 114. Specifically, the API server 116 provides a set ofinterfaces (e.g., routines and protocols) that can be called or queriedby the messaging client 104 in order to invoke functionality of theapplication servers 114. The API server 116 exposes various functionssupported by the application servers 114, including accountregistration; login functionality; the sending of messages, via theapplication servers 114, from a particular messaging client 104 toanother messaging client 104; the sending of media files (e.g., imagesor video) from a messaging client 104 to a messaging server 118, and forpossible access by another messaging client 104; the settings of acollection of media data (e.g., story); the retrieval of a list offriends of a user of a client device 102; the retrieval of suchcollections; the retrieval of messages and content; the addition anddeletion of entities (e.g., friends) to an entity graph (e.g., a socialgraph); the location of friends within a social graph; and opening anapplication event (e.g., relating to the messaging client 104).

The application servers 114 host a number of server applications andsubsystems, including, for example, a messaging server 118, an imageprocessing server 122, and a social network server 124. The messagingserver 118 implements a number of message processing technologies andfunctions, particularly related to the aggregation and other processingof content (e.g., textual and multimedia content) included in messagesreceived from multiple instances of the messaging client 104. As will bedescribed in further detail, the text and media content from multiplesources may be aggregated into collections of content (e.g., calledstories or galleries). These collections are then made available to themessaging client 104. Other processor- and memory-intensive processingof data may also be performed server-side by the messaging server 118,in view of the hardware requirements for such processing.

The application servers 114 also include an image processing server 122that is dedicated to performing various image processing operations,typically with respect to images or video within the payload of amessage sent from or received at the messaging server 118.

Image processing server 122 is used to implement scan functionality ofthe augmentation system 208 (shown in FIG. 2 ). Scan functionalityincludes activating and providing one or more AR experiences on a clientdevice 102 when an image is captured by the client device 102.Specifically, the messaging client 104 on the client device 102 can beused to activate a camera. The camera displays one or more real-timeimages or a video to a user along with one or more icons or identifiersof one or more AR experiences. The user can select a given one of theidentifiers to launch the corresponding AR experience or perform adesired image modification (e.g., replacing a garment being worn by auser in a video or recoloring the garment worn by the user in the videoor modifying the garment based on a gesture performed by the user).

The social network server 124 supports various social networkingfunctions and services and makes these functions and services availableto the messaging server 118. To this end, the social network server 124maintains and accesses an entity graph 308 (as shown in FIG. 3 ) withinthe database 126. Examples of functions and services supported by thesocial network server 124 include the identification of other users ofthe messaging system 100 with which a particular user has relationshipsor is “following,” and also the identification of other entities andinterests of a particular user.

Returning to the messaging client 104, features and functions of anexternal resource (e.g., a third-party application 109 or applet) aremade available to a user via an interface of the messaging client 104.The messaging client 104 receives a user selection of an option tolaunch or access features of an external resource (e.g., a third-partyresource), such as external apps 109. The external resource may be athird-party application (external apps 109) installed on the clientdevice 102 (e.g., a “native app”), or a small-scale version of thethird-party application (e.g., an “applet”) that is hosted on the clientdevice 102 or remote of the client device 102 (e.g., on third-partyservers 110). The small-scale version of the third-party applicationincludes a subset of features and functions of the third-partyapplication (e.g., the full-scale, native version of the third-partystandalone application) and is implemented using a markup-languagedocument. In one example, the small-scale version of the third-partyapplication (e.g., an “applet”) is a web-based, markup-language versionof the third-party application and is embedded in the messaging client104. In addition to using markup-language documents (e.g., a .*ml file),an applet may incorporate a scripting language (e.g., a .*js file or a.json file) and a style sheet (e.g., a .*ss file).

In response to receiving a user selection of the option to launch oraccess features of the external resource (external app 109), themessaging client 104 determines whether the selected external resourceis a web-based external resource or a locally-installed externalapplication. In some cases, external applications 109 that are locallyinstalled on the client device 102 can be launched independently of andseparately from the messaging client 104, such as by selecting an icon,corresponding to the external application 109, on a home screen of theclient device 102. Small-scale versions of such external applicationscan be launched or accessed via the messaging client 104 and, in someexamples, no or limited portions of the small-scale external applicationcan be accessed outside of the messaging client 104. The small-scaleexternal application can be launched by the messaging client 104receiving, from an external app(s) server 110, a markup-languagedocument associated with the small-scale external application andprocessing such a document.

In response to determining that the external resource is alocally-installed external application 109, the messaging client 104instructs the client device 102 to launch the external application 109by executing locally-stored code corresponding to the externalapplication 109. In response to determining that the external resourceis a web-based resource, the messaging client 104 communicates with theexternal app(s) servers 110 to obtain a markup-language documentcorresponding to the selected resource. The messaging client 104 thenprocesses the obtained markup-language document to present the web-basedexternal resource within a user interface of the messaging client 104.

The messaging client 104 can notify a user of the client device 102, orother users related to such a user (e.g., “friends”), of activity takingplace in one or more external resources. For example, the messagingclient 104 can provide participants in a conversation (e.g., a chatsession) in the messaging client 104 with notifications relating to thecurrent or recent use of an external resource by one or more members ofa group of users. One or more users can be invited to join in an activeexternal resource or to launch a recently-used but currently inactive(in the group of friends) external resource. The external resource canprovide participants in a conversation, each using a respectivemessaging client 104, with the ability to share an item, status, state,or location in an external resource with one or more members of a groupof users into a chat session. The shared item may be an interactive chatcard with which members of the chat can interact, for example, to launchthe corresponding external resource, view specific information withinthe external resource, or take the member of the chat to a specificlocation or state within the external resource. Within a given externalresource, response messages can be sent to users on the messaging client104. The external resource can selectively include different media itemsin the responses, based on a current context of the external resource.

The messaging client 104 can present a list of the available externalresources (e.g., third-party or external applications 109 or applets) toa user to launch or access a given external resource. This list can bepresented in a context-sensitive menu. For example, the iconsrepresenting different ones of the external application 109 (or applets)can vary based on how the menu is launched by the user (e.g., from aconversation interface or from a non-conversation interface).

The messaging client 104 can present to a user one or more ARexperiences that can be controlled and presented on an article ofclothing, such as a shirt (fashion item or upper garment), worn by aperson (or user) depicted in the image. As an example, the messagingclient 104 can detect a person in an image or video captured by theclient device 102. The messaging client 104 can segment an article ofclothing (or fashion item), such as a shirt, in the image or video.While the disclosed examples are discussed in relation to a shirt wornby a person (or user of the client device 102) depicted in an image orvideo, similar techniques can be applied to any other article ofclothing, upper garment, or fashion item, such as a dress, pants,shorts, skirts, jackets, t-shirts, blouses, glasses, jewelry, a hat, earmuffs, and so forth.

In response to segmenting the shirt, the messaging client 104 canextract an image portion corresponding to the segmented shirt. Theextracted image portion can be processed by a trained machine learningtechnique (e.g., a neural network) to estimate an angle of each pixel inthe image portion relative to a camera used to capture the image, suchas relative to a surface normal of the camera used to capture the imageor video. This enables the messaging client 104 to present one or moreAR elements on the shirt depicted in the image based on the estimatedangle of each pixel in the portion of the image relative to the cameraused to capture the image.

In one example, the messaging client 104 can determine that light isbeing focused on the fashion item (shirt or upper garment) from a firstdirection based on the estimated angle of each pixel. In response, themessaging client 104 can modify pixel values of the portion of thefashion item to re-focus the light on the fashion item from a seconddirection based on the estimated angle of each pixel. Specifically, themessaging client 104 can determine that a first pixel in the portion ofthe image is pointing towards a given direction relative to the cameraor surface normal of the camera. In such cases, the messaging client 104can modify the first pixel to render a reflection of the re-focusedlight from the second direction towards the given direction.

For example, the light in the image depicting the fashion item can befocused from a top of the image towards a bottom of the image. Namely, aspotlight can be presented above the person depicted in the image andcan point downwards towards the floor depicted in the image. In suchcases, the messaging client 104 can modify the pixel values to re-focusartificial light from the bottom towards the top. For example, themessaging client 104 can remove the spotlight or light coming from thetop of the image depicting the person and can render a display of ARlight originating from a floor depicted in the image. The messagingclient 104 can control reflections off of a fashion item or clothingworn by the person depicted in the image based on the estimated angle ofeach pixel corresponding to the fashion item relative to the camera.Specifically, the messaging client 104 can determine how light that isdirected towards each given pixel of the fashion item is reflected orabsorbed based on the corresponding angle of each pixel relative to thecamera and based on the angle of each pixel relative to the point oforigin of the AR light. This creates a realistic illusion that light isoriginating from a bottom of the image as the manner of reflection andabsorption of the light on the fashion item worn by the person depictedin the image is preserved.

In some examples, the messaging client 104 can select or receive aselection of an AR material for the fashion item and can replace amaterial of the fashion item with the AR material based on the estimatedangle of each pixel. In one example, the messaging client 104 candisplay a list of different AR materials and can receive a selection ofthe AR material from the list. The selection can be made verbally usingspeech input from the user or by receiving touch input from the usertouching a particular one of the AR materials in the list.

Specifically, the messaging client 104 can determine a light reflectionor absorption property of the AR material. In response, the messagingclient 104 can, for each pixel in the portion of the image, compute anew pixel value based on the light reflection or absorption property ofthe AR material and the estimated angle of the pixel. In this way, themessaging client 104 can apply different AR shades to the fashion itemdepicted in the image or video for each different type of the ARmaterial that is selected. In one example, the fashion item worn by theperson depicted in the image or video can be made of a cloth orcloth-like material and the messaging client 104 can replace the clothor cloth-like material with an AR material, such as gold, mirror, metal,water, or slime. In such cases, the light reflection and/or absorptionproperties of the fashion item worn by the person depicted in the imageor video is changed to correspond to the light reflection and/orabsorption properties of the AR material. To perform such AR changes,the messaging client 104 can modify each pixel value of the portion ofthe image that corresponds to the fashion item based on the estimatedangle of the corresponding pixel relative to the camera. The value ofthe pixel of the portion of the fashion item represents the direction atwhich light is reflected or absorbed by that portion of the fashion itemmade of the AR material and differs from the direction at which light isreflected or absorbed by that portion of the fashion item made by thecloth or cloth-like material.

The messaging client 104 continuously or periodically recomputes andre-estimates the angle of each pixel in the portion of the imagecorresponding to the fashion item. Specifically, the messaging client104 can track movement of the person depicted in the image or videoacross frames of the image or video. As the person moves, the messagingclient 104 can recompute and re-estimate the angle of each pixel in theportion of the image corresponding to the fashion item. The messagingclient 104 can continuously or periodically modify the AR elementspresented on the fashion item and specifically modify the way in whichlight is reflected or absorbed by the pixels corresponding to thefashion item based on changes to the angle of each pixel.

In some implementations, the messaging client 104 can apply a neuralnetwork to the portion of the image corresponding to the fashion item toestimate the angle of each pixel relative to the surface normal of thecamera used to capture the image or video. The neural network can betrained to establish a relationship between image portions depictingdifferent orientations of fashion items and pixel directions of thefashion items relative to surface normals of cameras used to capture theimage portions. As another example, the neural network can be trained toestablish a relationship between image portions depicting reflections oflight from different directions on fashion items and pixel directions ofthe fashion items relative to surface normals of cameras used to capturethe image portions. As another example, the neural network can betrained to estimate lighting conditions for each pixel in the portionwhen the AR light is applied from a new direction (a direction specifiedin input received from a user). In such cases, the neural network istrained to establish a relationship between image portions depictingfocusing of light on fashion items from different directions andlighting conditions of pixels of the fashion items.

System Architecture

FIG. 2 is a block diagram illustrating further details regarding themessaging system 100, according to some examples. Specifically, themessaging system 100 is shown to comprise the messaging client 104 andthe application servers 114. The messaging system 100 embodies a numberof subsystems, which are supported on the client side by the messagingclient 104 and on the sever side by the application servers 114. Thesesubsystems include, for example, an ephemeral timer system 202, acollection management system 204, an augmentation system 208, a mapsystem 210, a game system 212, and an external resource system 220.

The ephemeral timer system 202 is responsible for enforcing thetemporary or time-limited access to content by the messaging client 104and the messaging server 118. The ephemeral timer system 202incorporates a number of timers that, based on duration and displayparameters associated with a message, or collection of messages (e.g., astory), selectively enable access (e.g., for presentation and display)to messages and associated content via the messaging client 104. Furtherdetails regarding the operation of the ephemeral timer system 202 areprovided below.

The collection management system 204 is responsible for managing sets orcollections of media (e.g., collections of text, image video, and audiodata). A collection of content (e.g., messages, including images, video,text, and audio) may be organized into an “event gallery” or an “eventstory.” Such a collection may be made available for a specified timeperiod, such as the duration of an event to which the content relates.For example, content relating to a music concert may be made availableas a “story” for the duration of that music concert. The collectionmanagement system 204 may also be responsible for publishing an iconthat provides notification of the existence of a particular collectionto the user interface of the messaging client 104.

The collection management system 204 further includes a curationinterface 206 that allows a collection manager to manage and curate aparticular collection of content. For example, the curation interface206 enables an event organizer to curate a collection of contentrelating to a specific event (e.g., delete inappropriate content orredundant messages). Additionally, the collection management system 204employs machine vision (or image recognition technology) and contentrules to automatically curate a content collection. In certain examples,compensation may be paid to a user for the inclusion of user-generatedcontent into a collection. In such cases, the collection managementsystem 204 operates to automatically make payments to such users for theuse of their content.

The augmentation system 208 provides various functions that enable auser to augment (e.g., annotate or otherwise modify or edit) mediacontent associated with a message. For example, the augmentation system208 provides functions related to the generation and publishing of mediaoverlays for messages processed by the messaging system 100. Theaugmentation system 208 operatively supplies a media overlay oraugmentation (e.g., an image filter) to the messaging client 104 basedon a geolocation of the client device 102. In another example, theaugmentation system 208 operatively supplies a media overlay to themessaging client 104 based on other information, such as social networkinformation of the user of the client device 102. A media overlay mayinclude audio and visual content and visual effects. Examples of audioand visual content include pictures, texts, logos, animations, and soundeffects. An example of a visual effect includes color overlaying. Theaudio and visual content or the visual effects can be applied to a mediacontent item (e.g., a photo) at the client device 102. For example, themedia overlay may include text, a graphical element, or image that canbe overlaid on top of a photograph taken by the client device 102. Inanother example, the media overlay includes an identification of alocation overlay (e.g., Venice beach), a name of a live event, or a nameof a merchant overlay (e.g., Beach Coffee House). In another example,the augmentation system 208 uses the geolocation of the client device102 to identify a media overlay that includes the name of a merchant atthe geolocation of the client device 102. The media overlay may includeother indicia associated with the merchant. The media overlays may bestored in the database 126 and accessed through the database server 120.

In some examples, the augmentation system 208 provides a user-basedpublication platform that enables users to select a geolocation on a mapand upload content associated with the selected geolocation. The usermay also specify circumstances under which a particular media overlayshould be offered to other users. The augmentation system 208 generatesa media overlay that includes the uploaded content and associates theuploaded content with the selected geolocation.

In other examples, the augmentation system 208 provides a merchant-basedpublication platform that enables merchants to select a particular mediaoverlay associated with a geolocation via a bidding process. Forexample, the augmentation system 208 associates the media overlay of thehighest bidding merchant with a corresponding geolocation for apredefined amount of time. The augmentation system 208 communicates withthe image processing server 122 to obtain AR experiences and presentsidentifiers of such experiences in one or more user interfaces (e.g., asicons over a real-time image or video or as thumbnails or icons ininterfaces dedicated for presented identifiers of AR experiences). Oncean AR experience is selected, one or more images, videos, or ARgraphical elements are retrieved and presented as an overlay on top ofthe images or video captured by the client device 102. In some cases,the camera is switched to a front-facing view (e.g., the front-facingcamera of the client device 102 is activated in response to activationof a particular AR experience) and the images from the front-facingcamera of the client device 102 start being displayed on the clientdevice 102 instead of the rear-facing camera of the client device 102.The one or more images, videos, or AR graphical elements are retrievedand presented as an overlay on top of the images that are captured anddisplayed by the front-facing camera of the client device 102.

In other examples, the augmentation system 208 is able to communicateand exchange data with another augmentation system 208 on another clientdevice 102 and with the server via the network 112. The data exchangedcan include a session identifier that identifies the shared AR session,a transformation between a first client device 102 and a second clientdevice 102 (e.g., a plurality of client devices 102 include the firstand second devices) that is used to align the shared AR session to acommon point of origin, a common coordinate frame, functions (e.g.,commands to invoke functions), and other payload data (e.g., text,audio, video, or other multimedia data).

The augmentation system 208 sends the transformation to the secondclient device 102 so that the second client device 102 can adjust the ARcoordinate system based on the transformation. In this way, the firstand second client devices 102 synch up their coordinate systems andframes for displaying content in the AR session. Specifically, theaugmentation system 208 computes the point of origin of the secondclient device 102 in the coordinate system of the first client device102. The augmentation system 208 can then determine an offset in thecoordinate system of the second client device 102 based on the positionof the point of origin from the perspective of the second client device102 in the coordinate system of the second client device 102. Thisoffset is used to generate the transformation so that the second clientdevice 102 generates AR content according to a common coordinate systemor frame as the first client device 102.

The augmentation system 208 can communicate with the client device 102to establish individual or shared AR sessions. The augmentation system208 can also be coupled to the messaging server 118 to establish anelectronic group communication session (e.g., group chat, instantmessaging) for the client devices 102 in a shared AR session. Theelectronic group communication session can be associated with a sessionidentifier provided by the client devices 102 to gain access to theelectronic group communication session and to the shared AR session. Inone example, the client devices 102 first gain access to the electronicgroup communication session and then obtain the session identifier inthe electronic group communication session that allows the clientdevices 102 to access the shared AR session. In some examples, theclient devices 102 are able to access the shared AR session without aidor communication with the augmentation system 208 in the applicationservers 114.

The map system 210 provides various geographic location functions andsupports the presentation of map-based media content and messages by themessaging client 104. For example, the map system 210 enables thedisplay of user icons or avatars (e.g., stored in profile data 316) on amap to indicate a current or past location of “friends” of a user, aswell as media content (e.g., collections of messages includingphotographs and videos) generated by such friends, within the context ofa map. For example, a message posted by a user to the messaging system100 from a specific geographic location may be displayed within thecontext of a map at that particular location to “friends” of a specificuser on a map interface of the messaging client 104. A user canfurthermore share his or her location and status information (e.g.,using an appropriate status avatar) with other users of the messagingsystem 100 via the messaging client 104, with this location and statusinformation being similarly displayed within the context of a mapinterface of the messaging client 104 to selected users.

The game system 212 provides various gaming functions within the contextof the messaging client 104. The messaging client 104 provides a gameinterface providing a list of available games (e.g., web-based games orweb-based applications) that can be launched by a user within thecontext of the messaging client 104 and played with other users of themessaging system 100. The messaging system 100 further enables aparticular user to invite other users to participate in the play of aspecific game by issuing invitations to such other users from themessaging client 104. The messaging client 104 also supports both voiceand text messaging (e.g., chats) within the context of gameplay,provides a leaderboard for the games, and also supports the provision ofin-game rewards (e.g., coins and items).

The external resource system 220 provides an interface for the messagingclient 104 to communicate with external app(s) servers 110 to launch oraccess external resources. Each external resource (apps) server 110hosts, for example, a markup language (e.g., HTML5) based application orsmall-scale version of an external application (e.g., game, utility,payment, or ride-sharing application that is external to the messagingclient 104). The messaging client 104 may launch a web-based resource(e.g., application) by accessing the HTML5 file from the externalresource (apps) servers 110 associated with the web-based resource. Incertain examples, applications hosted by external resource servers 110are programmed in JavaScript leveraging a Software Development Kit (SDK)provided by the messaging server 118. The SDK includes APIs withfunctions that can be called or invoked by the web-based application. Incertain examples, the messaging server 118 includes a JavaScript librarythat provides a given third-party resource access to certain user dataof the messaging client 104. HTML5 is used as an example technology forprogramming games, but applications and resources programmed based onother technologies can be used.

In order to integrate the functions of the SDK into the web-basedresource, the SDK is downloaded by an external resource (apps) server110 from the messaging server 118 or is otherwise received by theexternal resource (apps) server 110. Once downloaded or received, theSDK is included as part of the application code of a web-based externalresource. The code of the web-based resource can then call or invokecertain functions of the SDK to integrate features of the messagingclient 104 into the web-based resource.

The SDK stored on the messaging server 118 effectively provides thebridge between an external resource (e.g., third-party or externalapplications 109 or applets and the messaging client 104). This providesthe user with a seamless experience of communicating with other users onthe messaging client 104, while also preserving the look and feel of themessaging client 104. To bridge communications between an externalresource and a messaging client 104, in certain examples, the SDKfacilitates communication between external resource servers 110 and themessaging client 104. In certain examples, a WebViewJavaScriptBridgerunning on a client device 102 establishes two one-way communicationchannels between an external resource and the messaging client 104.Messages are sent between the external resource and the messaging client104 via these communication channels asynchronously. Each SDK functioninvocation is sent as a message and callback. Each SDK function isimplemented by constructing a unique callback identifier and sending amessage with that callback identifier.

By using the SDK, not all information from the messaging client 104 isshared with external resource servers 110. The SDK limits whichinformation is shared based on the needs of the external resource. Incertain examples, each external resource server 110 provides an HTML5file corresponding to the web-based external resource to the messagingserver 118. The messaging server 118 can add a visual representation(such as a box art or other graphic) of the web-based external resourcein the messaging client 104. Once the user selects the visualrepresentation or instructs the messaging client 104 through a graphicaluser interface of the messaging client 104 to access features of theweb-based external resource, the messaging client 104 obtains the HTML5file and instantiates the resources necessary to access the features ofthe web-based external resource.

The messaging client 104 presents a graphical user interface (e.g., alanding page or title screen) for an external resource. During, before,or after presenting the landing page or title screen, the messagingclient 104 determines whether the launched external resource has beenpreviously authorized to access user data of the messaging client 104.In response to determining that the launched external resource has beenpreviously authorized to access user data of the messaging client 104,the messaging client 104 presents another graphical user interface ofthe external resource that includes functions and features of theexternal resource. In response to determining that the launched externalresource has not been previously authorized to access user data of themessaging client 104, after a threshold period of time (e.g., 3 seconds)of displaying the landing page or title screen of the external resource,the messaging client 104 slides up (e.g., animates a menu as surfacingfrom a bottom of the screen to a middle of or other portion of thescreen) a menu for authorizing the external resource to access the userdata. The menu identifies the type of user data that the externalresource will be authorized to use. In response to receiving a userselection of an accept option, the messaging client 104 adds theexternal resource to a list of authorized external resources and allowsthe external resource to access user data from the messaging client 104.In some examples, the external resource is authorized by the messagingclient 104 to access the user data in accordance with an OAuth 2framework.

The messaging client 104 controls the type of user data that is sharedwith external resources based on the type of external resource beingauthorized. For example, external resources that include full-scaleexternal applications (e.g., a third-party or external application 109)are provided with access to a first type of user data (e.g., onlytwo-dimensional (2D) avatars of users with or without different avatarcharacteristics). As another example, external resources that includesmall-scale versions of external applications (e.g., web-based versionsof third-party applications) are provided with access to a second typeof user data (e.g., payment information, 2D avatars of users,three-dimensional (3D) avatars of users, and avatars with various avatarcharacteristics). Avatar characteristics include different ways tocustomize a look and feel of an avatar, such as different poses, facialfeatures, clothing, and so forth.

An AR fashion control system 224 segments a fashion item, such as ashirt, worn by a user depicted in an image (or video) or multiplefashion items worn respectively by multiple users depicted in an image(or video). An illustrative implementation of the AR fashion controlsystem 224 is shown and described in connection with FIG. 5 below.

Specifically, the AR fashion control system 224 is a component that canbe accessed by an AR/VR application implemented on the client device102. The AR/VR application uses an RGB camera to capture a monocularimage of a user and the garment or garments (alternatively referred toas fashion item(s)) worn by the user. The AR/VR application appliesvarious trained machine learning techniques on the captured image of theuser wearing the garment to segment the garment (e.g., a shirt, jacket,pants, dress, and so forth) worn by the user in the image and to applyone or more AR visual effects (e.g., game-based AR elements) to thecaptured image. Segmenting the garment results in an outline of theborders of the garment that appear in the image or video. Pixels withinthe borders of the segmented garment correspond to the garment orclothing worn by the user. The segmented garment is used to distinguishthe clothing or garment worn by the user from other objects or elementsdepicted in the image, such as parts of the user's body (e.g., arms,head, legs, and so forth) and the background of the image which can beseparately segmented and tracked. In some implementations, the AR/VRapplication continuously captures images of the user wearing the garmentin real time or periodically to continuously or periodically update theapplied one or more visual effects. This allows the user to move aroundin the real world and see the one or more visual effects update in realtime.

In order for the AR/VR application to apply the one or more visualeffects directly from a captured RGB image, the AR/VR applicationobtains a trained machine learning technique from the AR fashion controlsystem 224. The trained machine learning technique processes thecaptured RGB image to generate a segmentation from the captured imagethat corresponds to the garment worn by the user(s) depicted in thecaptured RGB image.

In training, the AR fashion control system 224 obtains a first pluralityof input training images that include depictions of one or more userswearing different garments. These training images also provide theground truth information about the segmentations of the garments worn bythe users depicted in each image. A machine learning technique (e.g., adeep neural network) is trained based on features of the plurality oftraining images. Specifically, the first machine learning techniqueextracts one or more features from a given training image and estimatesa segmentation of the garment worn by the user depicted in the giventraining image. The machine learning technique obtains the ground truthinformation corresponding to the training image and adjusts or updatesone or more coefficients or parameters to improve subsequent estimationsof segmentations of the garment, such as the shirt.

Data Architecture

FIG. 3 is a schematic diagram illustrating data structures 300, whichmay be stored in the database 126 of the messaging server system 108,according to certain examples. While the content of the database 126 isshown to comprise a number of tables, it will be appreciated that thedata could be stored in other types of data structures (e.g., as anobject-oriented database).

The database 126 includes message data stored within a message table302. This message data includes, for any particular one message, atleast message sender data, message recipient (or receiver) data, and apayload. Further details regarding information that may be included in amessage, and included within the message data stored in the messagetable 302, are described below with reference to FIG. 4 .

An entity table 306 stores entity data, and is linked (e.g.,referentially) to an entity graph 308 and profile data 316. Entities forwhich records are maintained within the entity table 306 may includeindividuals, corporate entities, organizations, objects, places, events,and so forth. Regardless of entity type, any entity regarding which themessaging server system 108 stores data may be a recognized entity. Eachentity is provided with a unique identifier, as well as an entity typeidentifier (not shown).

The entity graph 308 stores information regarding relationships andassociations between entities. Such relationships may be social,professional (e.g., work at a common corporation or organization),interested-based, or activity-based, merely for example.

The profile data 316 stores multiple types of profile data about aparticular entity. The profile data 316 may be selectively used andpresented to other users of the messaging system 100, based on privacysettings specified by a particular entity. Where the entity is anindividual, the profile data 316 includes, for example, a user name,telephone number, address, and settings (e.g., notification and privacysettings), as well as a user-selected avatar representation (orcollection of such avatar representations). A particular user may thenselectively include one or more of these avatar representations withinthe content of messages communicated via the messaging system 100 and onmap interfaces displayed by messaging clients 104 to other users. Thecollection of avatar representations may include “status avatars,” whichpresent a graphical representation of a status or activity that the usermay select to communicate at a particular time.

Where the entity is a group, the profile data 316 for the group maysimilarly include one or more avatar representations associated with thegroup, in addition to the group name, members, and various settings(e.g., notifications) for the relevant group.

The database 126 also stores augmentation data, such as overlays orfilters, in an augmentation table 310. The augmentation data isassociated with and applied to videos (for which data is stored in avideo table 304) and images (for which data is stored in an image table312).

The database 126 can also store data pertaining to individual and sharedAR sessions. This data can include data communicated between an ARsession client controller of a first client device 102 and another ARsession client controller of a second client device 102, and datacommunicated between the AR session client controller and theaugmentation system 208. Data can include data used to establish thecommon coordinate frame of the shared AR scene, the transformationbetween the devices, the session identifier, images depicting a body,skeletal joint positions, wrist joint positions, feet, and so forth.

Filters, in one example, are overlays that are displayed as overlaid onan image or video during presentation to a recipient user. Filters maybe of various types, including user-selected filters from a set offilters presented to a sending user by the messaging client 104 when thesending user is composing a message. Other types of filters includegeolocation filters (also known as geo-filters), which may be presentedto a sending user based on geographic location. For example, geolocationfilters specific to a neighborhood or special location may be presentedwithin a user interface by the messaging client 104, based ongeolocation information determined by a Global Positioning System (GPS)unit of the client device 102.

Another type of filter is a data filter, which may be selectivelypresented to a sending user by the messaging client 104, based on otherinputs or information gathered by the client device 102 during themessage creation process. Examples of data filters include currenttemperature at a specific location, a current speed at which a sendinguser is traveling, battery life for a client device 102, or the currenttime.

Other augmentation data that may be stored within the image table 312includes AR content items (e.g., corresponding to applying ARexperiences). An AR content item or AR item may be a real-time specialeffect and sound that may be added to an image or a video.

As described above, augmentation data includes AR content items,overlays, image transformations, AR images, AR logos or emblems, andsimilar terms that refer to modifications that may be applied to imagedata (e.g., videos or images). This includes real-time modifications,which modify an image as it is captured using device sensors (e.g., oneor multiple cameras) of a client device 102 and then displayed on ascreen of the client device 102 with the modifications. This alsoincludes modifications to stored content, such as video clips in agallery that may be modified. For example, in a client device 102 withaccess to multiple AR content items, a user can use a single video clipwith multiple AR content items to see how the different AR content itemswill modify the stored clip. For example, multiple AR content items thatapply different pseudorandom movement models can be applied to the samecontent by selecting different AR content items for the content.Similarly, real-time video capture may be used with an illustratedmodification to show how video images currently being captured bysensors of a client device 102 would modify the captured data. Such datamay simply be displayed on the screen and not stored in memory, or thecontent captured by the device sensors may be recorded and stored inmemory with or without the modifications (or both). In some systems, apreview feature can show how different AR content items will look withindifferent windows in a display at the same time. This can, for example,enable multiple windows with different pseudorandom animations to beviewed on a display at the same time.

Data and various systems using AR content items or other such transformsystems to modify content using this data can thus involve detection ofobjects (e.g., faces, hands, bodies, cats, dogs, surfaces, objects,etc.), tracking of such objects as they leave, enter, and move aroundthe field of view in video frames, and the modification ortransformation of such objects as they are tracked. In various examples,different methods for achieving such transformations may be used. Someexamples may involve generating a 3D mesh model of the object or objectsand using transformations and animated textures of the model within thevideo to achieve the transformation. In other examples, tracking ofpoints on an object may be used to place an image or texture (which maybe 2D or 3D) at the tracked position. In still further examples, neuralnetwork analysis of video frames may be used to place images, models, ortextures in content (e.g., images or frames of video). AR content itemsthus refer both to the images, models, and textures used to createtransformations in content, as well as to additional modeling andanalysis information needed to achieve such transformations with objectdetection, tracking, and placement.

Real-time video processing can be performed with any kind of video data(e.g., video streams, video files, etc.) saved in a memory of acomputerized system of any kind. For example, a user can load videofiles and save them in a memory of a device or can generate a videostream using sensors of the device. Additionally, any objects can beprocessed using a computer animation model, such as a human's face andparts of a human body, animals, or non-living things such as chairs,cars, or other objects.

In some examples, when a particular modification is selected along withcontent to be transformed, elements to be transformed are identified bythe computing device and then detected and tracked if they are presentin the frames of the video. The elements of the object are modifiedaccording to the request for modification, thus transforming the framesof the video stream. Transformation of frames of a video stream can beperformed by different methods for different kinds of transformation.For example, for transformations of frames mostly referring to changingforms of an object's elements, characteristic points for each element ofan object are calculated (e.g., using an Active Shape Model (ASM) orother known methods). Then, a mesh based on the characteristic points isgenerated for each of the at least one element of the object. This meshis used in the following stage of tracking the elements of the object inthe video stream. In the process of tracking, the mentioned mesh foreach element is aligned with a position of each element. Then,additional points are generated on the mesh. A first set of first pointsis generated for each element based on a request for modification, and aset of second points is generated for each element based on the set offirst points and the request for modification. Then, the frames of thevideo stream can be transformed by modifying the elements of the objecton the basis of the sets of first and second points and the mesh. Insuch method, a background of the modified object can be changed ordistorted as well by tracking and modifying the background.

In some examples, transformations changing some areas of an object usingits elements can be performed by calculating characteristic points foreach element of an object and generating a mesh based on the calculatedcharacteristic points. Points are generated on the mesh and then variousareas based on the points are generated. The elements of the object arethen tracked by aligning the area for each element with a position foreach of the at least one elements, and properties of the areas can bemodified based on the request for modification, thus transforming theframes of the video stream. Depending on the specific request formodification, properties of the mentioned areas can be transformed indifferent ways. Such modifications may involve changing color of areas;removing at least some part of areas from the frames of the videostream; including one or more new objects into areas which are based ona request for modification; and modifying or distorting the elements ofan area or object. In various examples, any combination of suchmodifications or other similar modifications may be used. For certainmodels to be animated, some characteristic points can be selected ascontrol points to be used in determining the entire state-space ofoptions for the model animation.

In some examples of a computer animation model to transform image datausing face detection, the face is detected on an image with use of aspecific face detection algorithm (e.g., Viola-Jones). Then, an ASMalgorithm is applied to the face region of an image to detect facialfeature reference points.

Other methods and algorithms suitable for face detection can be used.For example, in some examples, features are located using a landmark,which represents a distinguishable point present in most of the imagesunder consideration. For facial landmarks, for example, the location ofthe left eye pupil may be used. If an initial landmark is notidentifiable (e.g., if a person has an eyepatch), secondary landmarksmay be used. Such landmark identification procedures may be used for anysuch objects. In some examples, a set of landmarks forms a shape. Shapescan be represented as vectors using the coordinates of the points in theshape. One shape is aligned to another with a similarity transform(allowing translation, scaling, and rotation) that minimizes the averageEuclidean distance between shape points. The mean shape is the mean ofthe aligned training shapes.

In some examples, a search is started for landmarks from the mean shapealigned to the position and size of the face determined by a global facedetector. Such a search then repeats the steps of suggesting a tentativeshape by adjusting the locations of shape points by template matching ofthe image texture around each point and then conforming the tentativeshape to a global shape model until convergence occurs. In some systems,individual template matches are unreliable, and the shape model poolsthe results of the weak template matches to form a stronger overallclassifier. The entire search is repeated at each level in an imagepyramid, from coarse to fine resolution.

A transformation system can capture an image or video stream on a clientdevice (e.g., the client device 102) and perform complex imagemanipulations locally on the client device 102 while maintaining asuitable user experience, computation time, and power consumption. Thecomplex image manipulations may include size and shape changes, emotiontransfers (e.g., changing a face from a frown to a smile), statetransfers (e.g., aging a subject, reducing apparent age, changinggender), style transfers, graphical element application, and any othersuitable image or video manipulation implemented by a convolutionalneural network that has been configured to execute efficiently on theclient device 102.

In some examples, a computer animation model to transform image data canbe used by a system where a user may capture an image or video stream ofthe user (e.g., a selfie) using a client device 102 having a neuralnetwork operating as part of a messaging client 104 operating on theclient device 102. The transformation system operating within themessaging client 104 determines the presence of a face within the imageor video stream and provides modification icons associated with acomputer animation model to transform image data, or the computeranimation model can be present as associated with an interface describedherein. The modification icons include changes that may be the basis formodifying the user's face within the image or video stream as part ofthe modification operation. Once a modification icon is selected, thetransformation system initiates a process to convert the image of theuser to reflect the selected modification icon (e.g., generate a smilingface on the user). A modified image or video stream may be presented ina graphical user interface displayed on the client device 102 as soon asthe image or video stream is captured and a specified modification isselected. The transformation system may implement a complexconvolutional neural network on a portion of the image or video streamto generate and apply the selected modification. That is, the user maycapture the image or video stream and be presented with a modifiedresult in real-time or near real-time once a modification icon has beenselected. Further, the modification may be persistent while the videostream is being captured and the selected modification icon remainstoggled. Machine-taught neural networks may be used to enable suchmodifications.

The graphical user interface, presenting the modification performed bythe transformation system, may supply the user with additionalinteraction options. Such options may be based on the interface used toinitiate the content capture and selection of a particular computeranimation model (e.g., initiation from a content creator userinterface). In various examples, a modification may be persistent afteran initial selection of a modification icon. The user may toggle themodification on or off by tapping or otherwise selecting the face beingmodified by the transformation system and store it for later viewing orbrowse to other areas of the imaging application. Where multiple facesare modified by the transformation system, the user may toggle themodification on or off globally by tapping or selecting a single facemodified and displayed within a graphical user interface. In someexamples, individual faces, among a group of multiple faces, may beindividually modified, or such modifications may be individually toggledby tapping or selecting the individual face or a series of individualfaces displayed within the graphical user interface.

A story table 314 stores data regarding collections of messages andassociated image, video, or audio data, which are compiled into acollection (e.g., a story or a gallery). The creation of a particularcollection may be initiated by a particular user (e.g., each user forwhich a record is maintained in the entity table 306). A user may createa “personal story” in the form of a collection of content that has beencreated and sent/broadcast by that user. To this end, the user interfaceof the messaging client 104 may include an icon that is user-selectableto enable a sending user to add specific content to his or her personalstory.

A collection may also constitute a “live story,” which is a collectionof content from multiple users that is created manually, automatically,or using a combination of manual and automatic techniques. For example,a “live story” may constitute a curated stream of user-submitted contentfrom various locations and events. Users whose client devices havelocation services enabled and are at a common location event at aparticular time may, for example, be presented with an option, via auser interface of the messaging client 104, to contribute content to aparticular live story. The live story may be identified to the user bythe messaging client 104, based on his or her location. The end resultis a “live story” told from a community perspective.

A further type of content collection is known as a “location story,”which enables a user whose client device 102 is located within aspecific geographic location (e.g., on a college or university campus)to contribute to a particular collection. In some examples, acontribution to a location story may require a second degree ofauthentication to verify that the end user belongs to a specificorganization or other entity (e.g., is a student on the universitycampus).

As mentioned above, the video table 304 stores video data that, in oneexample, is associated with messages for which records are maintainedwithin the message table 302. Similarly, the image table 312 storesimage data associated with messages for which message data is stored inthe entity table 306. The entity table 306 may associate variousaugmentations from the augmentation table 310 with various images andvideos stored in the image table 312 and the video table 304.

Trained machine learning technique(s) 307 stores parameters that havebeen trained during training of the AR fashion control system 224. Forexample, trained machine learning techniques 307 stores the trainedparameters of one or more neural network machine learning techniques.

Segmentation training images 309 stores a plurality of images that eachdepict one or more users wearing different garments. The plurality ofimages stored in the segmentation training images 309 includes variousdepictions of one or more users wearing different garments together withsegmentations of the garments that indicate which pixels in the imagescorrespond to the garments and which pixels correspond to a backgroundor a user's body parts in the images. Namely the segmentations providethe borders of the garments depicted in the images. These segmentationtraining images 309 are used by the AR fashion control system 224 totrain the machine learning technique used to generate a segmentation ofone or more garments depicted in a received RGB monocular image. In somecases, the segmentation training images 309 include ground truthskeletal key points of one or more bodies depicted in the respectivetraining monocular images to enhance segmentation performance on variousdistinguishing attributes (e.g., shoulder straps, collar or sleeves) ofthe garments. In some cases, the segmentation training images 309include a plurality of image resolutions of bodies depicted in theimages. The segmentation training images 309 can include labeled andunlabeled image and video data. The segmentation training images 309 caninclude a depiction of a whole body of a particular user, an image thatlacks a depiction of any user (e.g., a negative image), a depiction of aplurality of users wearing different garments, and depictions of userswearing garments at different distances from an image capture device.

Segmentation training images 309 can also store training image portionsdepicting different orientations of fashion items and correspondingground-truth pixel directions of the fashion items (including differenttypes of fashion items) relative to surface normals of cameras used tocapture the training image portions. Segmentation training images 309can also store training image portions depicting reflections of lightfrom different directions on fashion items (including different types offashion items) and corresponding ground-truth pixel directions of thefashion items relative to surface normals of cameras used to capture thetraining image portions. Segmentation training images 309 can also storetraining image portions depicting focusing of light on fashion items(including different types of fashion items) from different directionsand corresponding ground-truth lighting conditions of pixels of thefashion items

Data Communications Architecture

FIG. 4 is a schematic diagram illustrating a structure of a message 400,according to some examples, generated by a messaging client 104 forcommunication to a further messaging client 104 or the messaging server118. The content of a particular message 400 is used to populate themessage table 302 stored within the database 126, accessible by themessaging server 118. Similarly, the content of a message 400 is storedin memory as “in-transit” or “in-flight” data of the client device 102or the application servers 114. A message 400 is shown to include thefollowing example components:

-   -   message identifier 402: a unique identifier that identifies the        message 400.    -   message text payload 404: text, to be generated by a user via a        user interface of the client device 102, and that is included in        the message 400.    -   message image payload 406: image data, captured by a camera        component of a client device 102 or retrieved from a memory        component of a client device 102, and that is included in the        message 400. Image data for a sent or received message 400 may        be stored in the image table 312.    -   message video payload 408: video data, captured by a camera        component or retrieved from a memory component of the client        device 102, and that is included in the message 400. Video data        for a sent or received message 400 may be stored in the video        table 304.    -   message audio payload 410: audio data, captured by a microphone        or retrieved from a memory component of the client device 102,        and that is included in the message 400.    -   message augmentation data 412: augmentation data (e.g., filters,        stickers, or other annotations or enhancements) that represents        augmentations to be applied to message image payload 406,        message video payload 408, or message audio payload 410 of the        message 400. Augmentation data for a sent or received message        400 may be stored in the augmentation table 310.    -   message duration parameter 414: parameter value indicating, in        seconds, the amount of time for which content of the message        (e.g., the message image payload 406, message video payload 408,        message audio payload 410) is to be presented or made accessible        to a user via the messaging client 104.    -   message geolocation parameter 416: geolocation data (e.g.,        latitudinal and longitudinal coordinates) associated with the        content payload of the message. Multiple message geolocation        parameter 416 values may be included in the payload, each of        these parameter values being associated with respect to content        items included in the content (e.g., a specific image within the        message image payload 406, or a specific video in the message        video payload 408).    -   message story identifier 418: identifier values identifying one        or more content collections (e.g., “stories” identified in the        story table 314) with which a particular content item in the        message image payload 406 of the message 400 is associated. For        example, multiple images within the message image payload 406        may each be associated with multiple content collections using        identifier values.    -   message tag 420: each message 400 may be tagged with multiple        tags, each of which is indicative of the subject matter of        content included in the message payload. For example, where a        particular image included in the message image payload 406        depicts an animal (e.g., a lion), a tag value may be included        within the message tag 420 that is indicative of the relevant        animal. Tag values may be generated manually, based on user        input, or may be automatically generated using, for example,        image recognition.    -   message sender identifier 422: an identifier (e.g., a messaging        system identifier, email address, or device identifier)        indicative of a user of the client device 102 on which the        message 400 was generated and from which the message 400 was        sent.    -   message receiver identifier 424: an identifier (e.g., a        messaging system identifier, email address, or device        identifier) indicative of a user of the client device 102 to        which the message 400 is addressed.

The contents (e.g., values) of the various components of message 400 maybe pointers to locations in tables within which content data values arestored. For example, an image value in the message image payload 406 maybe a pointer to (or address of) a location within an image table 312.Similarly, values within the message video payload 408 may point to datastored within a video table 304, values stored within the messageaugmentation data 412 may point to data stored in an augmentation table310, values stored within the message story identifier 418 may point todata stored in a story table 314, and values stored within the messagesender identifier 422 and the message receiver identifier 424 may pointto user records stored within an entity table 306.

AR Fashion Control System

FIG. 5 is a block diagram showing an example AR fashion control system224, according to example examples. AR fashion control system 224includes a set of components 510 that operate on a set of input data(e.g., a monocular image 501 depicting a real body of a user wearing ashirt and upper segmentation training image data 502). The set of inputdata is obtained from segmentation training images 309 stored indatabase(s) (FIG. 3 ) during the training phases and is obtained from anRGB camera of a client device 102 when an AR/VR application is beingused, such as by a messaging client 104. AR fashion control system 224includes a machine learning technique module 512, a skeletal key-pointsmodule 511, an upper garment segmentation module 514, a pixel angleestimation module 517, an image modification module 518, an AR effectmodule 519, a 3D body tracking module 513, a whole-body segmentationmodule 515, and an image display module 520.

During training, the AR fashion control system 224 receives a giventraining image or video (e.g., monocular image 501 depicting a real bodyof a user wearing a garment, such as an image of a user wearing as ashirt (short sleeve, t-shirt, or long sleeve), jacket, tank top,sweater, and so forth, a lower body garment, such as pants or a skirt, awhole body garment, such as a dress or overcoat, or any suitablecombination thereof or depicting multiple users simultaneously wearingrespective combinations of upper body garments, lower body garments, orwhole body garments from segmentation training image data 502. The ARfashion control system 224 applies one or more machine learningtechniques using the machine learning technique module 512 on the giventraining image or video. The machine learning technique module 512extracts one or more features from the given training image or video toestimate a segmentation of the garment(s) worn by the user(s) depictedin the image. For example, the machine learning technique module 512obtains the given training image or video depicting a user wearing ashirt. The machine learning technique module 512 extracts features fromthe image and segments or specifies which pixels in the image correspondto the shirt worn by the user and which pixels correspond to abackground or correspond to parts of the user's body. Namely, thesegmentation output by the machine learning technique module 512identifies borders of a garment (e.g., the shirt) worn by the user inthe given training image.

The machine learning technique module 512 retrieves garment segmentationinformation associated with the given training image or video. Themachine learning technique module 512 compares the estimatedsegmentation (that can include an identification of multiple garmentsworn by respective users in the image in case there exist multiple usersin the image) with the ground truth garment segmentation provided aspart of the segmentation training image data 502. Based on a differencethreshold or deviation of the comparison, the machine learning techniquemodule 512 updates one or more coefficients or parameters and obtainsone or more additional segmentation training images or videos. After aspecified number of epochs or batches of training images have beenprocessed and/or when the difference threshold or deviation reaches aspecified value, the machine learning technique module 512 completestraining and the parameters and coefficients of the machine learningtechnique module 512 are stored in the trained machine learningtechnique(s) 307.

In some examples, the machine learning technique module 512 implementsmultiple segmentation models of the machine learning technique. Eachsegmentation model of the machine learning technique module 512 may betrained on a different set of training images associated with a specificresolution. Namely, one of the segmentation models can be trained toestimate a garment segmentation for images having a first resolution (ora first range of resolutions). A second of the segmentation models canbe trained to estimate a garment segmentation for images having a secondresolution (or a second range of resolutions different from the firstrange of resolutions). In this way, different complexities of themachine learning technique module 512 can be trained and stored. When agiven device having certain capabilities uses the AR/VR application, acorresponding one of the various garment segmentation models can beprovided to perform the garment segmentation that matches thecapabilities of the given device. In some cases, multiple garmentsegmentation models of each of the machine leaning techniquesimplemented by the AR fashion control system 224 can be provided, witheach configured to operate with a different level of complexity. Theappropriate segmentation model(s) with the appropriate level ofcomplexity can then be provided to a client device 102 for segmentinggarments depicted in one or more images.

In some examples, during training, the machine learning technique module512 receives 2D skeletal joint information from a skeletal key-pointsmodule 511. The skeletal key-points module 511 tracks skeletal keypoints of a user depicted in a given training image (e.g., head joint,shoulder joints, hip joints, leg joints, and so forth) and provides the2D or 3D coordinates of the skeletal key points. This information isused by the machine learning technique module 512 to identifydistinguishing attributes of the garment depicted in the training image.

The garment segmentation generated by the machine learning techniquemodule 512 is provided to the upper garment segmentation module 514. Theupper garment segmentation module 514 can determine that the elbow jointoutput by the skeletal key-points module 511 is at a position that iswithin a threshold distance away from a given edge of the border of theshirt garment segmentation. In response, the upper garment segmentationmodule 514 can determine that the garment corresponds to a t-shirt orshort sleeve shirt and that the given edge corresponds to a sleeve ofthe shirt. In such circumstances, the upper garment segmentation module514 can adjust weights of the parameters or the loss function used toupdate parameters of the machine learning technique module 512 toimprove segmentation of upper body garments, such as shirts. Morespecifically, the upper garment segmentation module 514 can determinethat a given distinguishing attribute is present in the garmentsegmentation that is generated based on a comparison of skeletal jointpositions to borders of the garment segmentation. In such circumstances,the upper garment segmentation module 514 adjusts the loss function orweights used to update the parameters of the machine learning techniquemodule 512 for the training image depicting the garment with thedistinguishing attribute. Similarly, the upper garment segmentationmodule 514 can adjust the loss or the parameter weights based on adifference between the garment segmentation and the pixels correspondingto the background of the image.

The upper garment segmentation module 514 is used to track a 2D or 3Dposition of the segmented shirt in subsequent frames of a video. Thisenables one or more AR elements to be displayed on the shirt and bemaintained at their respective positions on the shirt as the position ofthe shirt moves around the screen. In this way, the upper garmentsegmentation module 514 can determine and track which portions of theshirt are currently shown in the image that depicts the user and toselectively adjust the corresponding AR elements that are displayed. Forexample, a given AR element can be displayed on a left sleeve of theshirt in a first frame of the video. The upper garment segmentationmodule 514 can determine that the user has turned left in a second frameof the video, meaning that the left sleeve no longer appears in thesecond frame. In response, the upper garment segmentation module 514 canomit entirely or a portion of the given AR element that was displayed onthe left sleeve of the shirt.

After training, AR fashion control system 224 receives an input image501 (e.g., monocular image depicting a user wearing a garment ormultiple users wearing respective garments) as a single RGB image from aclient device 102. The AR fashion control system 224 applies the trainedmachine learning technique module 512 to the received input image 501 toextract one or more features of the image to generate a segmentation ofthe garment or garments depicted in the image 501. This segmentation isprovided to the upper garment segmentation module 514 to track the 2D or3D position of the shirt (upper garment) in the current frame of thevideo and in subsequent frames.

FIG. 6 is a diagrammatic representation of outputs of the AR fashioncontrol system 224, in accordance with some examples. Specifically, FIG.6 shows a garment segmentation 600 generated by the upper garmentsegmentation module 514. In one example, the upper garment segmentationmodule 514 generates a first garment segmentation 612 representing pixellocations of a shirt (upper garment) worn by a user. In another example,the upper garment segmentation module 514 generates a second garmentsegmentation representing pixel locations of a short sleeve shirt (uppergarment) worn by a user. In another example, the upper garmentsegmentation module 514 generates a third garment segmentationrepresenting pixel locations of a jacket (upper garment) worn by a user.

The upper garment segmentation module 514 applies the segmentation ofthe upper garment (or any other garment worn by a person depicted in animage or video) to the image or video. The upper garment segmentationmodule 514 can extract or cut out that portion of the image or videothat corresponds to the upper garment segmentation. For example, theupper garment segmentation module 514 can generate an image portion thatonly includes the fashion item worn by the person depicted in the imageor video corresponding to the upper garment segmentation. While thedisclosed techniques are discussed in relation to an upper garment, anyother garment segmentation can be generated and used in similar ways.The image portion that depicts the upper garment is provided to thepixel angle estimation module 517.

The pixel angle estimation module 517 is trained to generate valuesindicating the pixel direction or angle relative to a camera used tocapture the image or video depicting the image portion corresponding tothe fashion item worn by the person or user depicted in the image. Theangle to the camera can in some cases be represented as a normal vectoror normal direction of a given pixel. For example, the pixel angleestimation module 517 can determine, for a given pixel, the direction towhich the pixel points relative to a surface normal of a camera thatcaptures the image that includes the pixel. This direction can beassociated with the pixel by storing the direction in a vector thatincludes or defines an x, y, z component in a red, green and blue (RGB)channel of each pixel. This can be referred to as the normal map.Namely, each pixel can be represented by an RGB channel that defines theamount of red, green and blue color to associate with the pixel, such asthe red, green and blue pixel values for each pixel. This RGB channelcan also be associated with a vector that defines the pixel or normaldirection of each pixel in the x, y, z coordinate or UV space. In thisway, each pixel can include red, green and blue values as well as x, yand z coordinates.

As an example, if the pixel is on a left shirt sleeve and the personwearing the shirt is turned left relative to the camera, the pixel anglecan be 45 degrees or −45 degrees (or any other suitable angle even anangle that is not facing from the camera) relative to a surface normalof the camera. A larger pixel angle indicates that the portion of thefashion item represented by the corresponding pixel is turned furtherright/left relative to the camera. The pixel angle can beone-dimensional and/or 2D. In case of being 2D, the pixel anglerepresents how much the fashion item is turned left/right relative tothe camera and also how far up/down the portion of the fashion item ispointing. The pixel angle estimation module 517 generates a matrixrepresenting the one-dimensional, 2D pixel angle, and/or 3D pixel anglefor each pixel in the portion of the image corresponding to the fashionitem or garment segmentation. The pixel angle can be represented by a 3Dnormal vector or 2D normal vector and stored in the RGB channel of eachpixel. In some cases, the pixel angle estimation module 517 generatesthe values discussed in this disclosure independently from movement ortracking information provided by the 3D body tracking module 513.

In some examples, the pixel angle estimation module 517 implements aneural network. The neural network can process a portion of an imagecorresponding to the fashion item to estimate the angle of each pixelrelative to the surface normal of the camera used to capture the imageor video. The neural network can be trained to establish a relationshipbetween image portions depicting different orientations of fashion itemsand pixel directions of the fashion items relative to surface normals ofcameras used to capture the image portions. Namely, during training, thepixel angle estimation module 517 receives a given training image orvideo (e.g., image portion depicting a real body of a user wearing agarment, such as an image of a user wearing as a shirt (short sleeve,t-shirt, or long sleeve), jacket, tank top, sweater, and so forth, alower body garment, such as pants or a skirt, a whole body garment, suchas a dress or overcoat, or any suitable combination thereof or depictingmultiple users simultaneously wearing respective combinations of upperbody garments, lower body garments or whole body garments) fromsegmentation training image data 502. The pixel angle estimation module517 applies one or more machine learning techniques on the giventraining image or video. The pixel angle estimation module 517 extractsone or more features from the given training image or video to estimatethe angle of each pixel relative to the surface normal of the cameraused to capture the image or video. For example, the pixel angleestimation module 517 can generate a matrix representing theone-dimensional or 2D pixel angle for each pixel in the portion of theimage that is processed.

The pixel angle estimation module 517 retrieves ground-truth pixel angleinformation (e.g., ground-truth matrix data representing the angle ofeach pixel in the image portion) associated with the given trainingimage or video. The pixel angle estimation module 517 compares theestimated angles of each pixel with the ground truth pixel angleinformation provided as part of the segmentation training image data502. Based on a difference threshold or deviation of the comparison, thepixel angle estimation module 517 updates one or more coefficients orparameters and obtains one or more additional segmentation trainingimages or videos. After a specified number of epochs or batches oftraining images have been processed and/or when the difference thresholdor deviation reaches a specified value, the pixel angle estimationmodule 517 completes training and the parameters and coefficients of thepixel angle estimation module 517 are stored in the trained machinelearning technique(s) 307.

As another example, the neural network can be trained to establish arelationship between image portions depicting reflections of light fromdifferent directions on fashion items and pixel directions of thefashion items relative to surface normals of cameras used to capture theimage portions. In such cases, during training, the pixel angleestimation module 517 receives a given training image or video (e.g.,image portions depicting reflections/absorptions of light from differentdirections on fashion items) from segmentation training image data 502.The pixel angle estimation module 517 applies one or more machinelearning techniques on the given training image or video. The pixelangle estimation module 517 extracts one or more features from the giventraining image or video to estimate the angle of each pixel relative tothe surface normal of the camera used to capture the image or video. Forexample, the pixel angle estimation module 517 can generate a matrixrepresenting the one-dimensional or 2D pixel angle for each pixel in theportion of the image that is processed.

The pixel angle estimation module 517 retrieves ground-truth pixel angleinformation (e.g., ground-truth matrix data representing the angle ofeach pixel in the image portion) associated with the given trainingimage or video. The pixel angle estimation module 517 compares theestimated angles of each pixel with the ground truth pixel angleinformation provided as part of the segmentation training image data502. Based on a difference threshold or deviation of the comparison, thepixel angle estimation module 517 updates one or more coefficients orparameters and obtains one or more additional segmentation trainingimages or videos. After a specified number of epochs or batches oftraining images have been processed and/or when the difference thresholdor deviation reaches a specified value, the pixel angle estimationmodule 517 completes training and the parameters and coefficients of thepixel angle estimation module 517 are stored in the trained machinelearning technique(s) 307.

As another example, the neural network can be trained to estimatelighting conditions for each pixel in the portion when the AR light isapplied from a new direction (a direction specified in input receivedfrom a user). In such cases, the neural network is trained to establisha relationship between image portions depicting focusing of light onfashion items from different directions and lighting conditions ofpixels of the fashion items. Namely, during training, the pixel angleestimation module 517 receives a given training image or video (e.g.,image portions depicting reflections of light from different directionsand lighting conditions on fashion items) from segmentation trainingimage data 502. The pixel angle estimation module 517 applies one ormore machine learning techniques on the given training image or video.The pixel angle estimation module 517 extracts one or more features fromthe given training image or video to estimate the focusing of light onfashion items from new directions and lighting conditions of pixels ofthe fashion items. Specifically, the pixel angle estimation module 517receives a given training image or video depicting reflections of lightfrom a first direction and first lighting conditions on fashion items.The pixel angle estimation module 517 applies one or more machinelearning techniques on the given training image or video. The pixelangle estimation module 517 extracts one or more features from the giventraining image or video to estimate the focusing of light on fashionitems from a second direction and second lighting conditions of pixelsof the fashion items.

The pixel angle estimation module 517 retrieves ground-truth pixellighting conditions for the new direction associated with the giventraining image or video. The pixel angle estimation module 517 comparesthe estimated lighting conditions of each pixel with the ground truthpixel lighting conditions provided as part of the segmentation trainingimage data 502. Based on a difference threshold or deviation of thecomparison, the pixel angle estimation module 517 updates one or morecoefficients or parameters and obtains one or more additionalsegmentation training images or videos. After a specified number ofepochs or batches of training images have been processed and/or when thedifference threshold or deviation reaches a specified value, the pixelangle estimation module 517 completes training and the parameters andcoefficients of the pixel angle estimation module 517 are stored in thetrained machine learning technique(s) 307.

In some cases, the pixel angle estimation module 517 is trained todetermine reflection and/or absorption of light for different materialproperties of fashion items. In such cases, the pixel angle estimationmodule 517 implements a neural network that establishes a relationshipbetween material properties of fashion items and reflection and/orabsorption amounts and/or pixel angles relative to a camera surfacenormal used to capture an image depicting the fashion item. The pixelangle estimation module 517 is trained to process various types offashion item materials (e.g., gold, water, mirror, and so forth) toestimate the reflection and/or absorption amounts and/or pixel anglesrelative to a camera surface normal used to capture an image depictingthe fashion item. The estimated reflection and/or absorption amountsand/or pixel angles relative to a camera surface normal used to capturean image depicting the fashion item are compared with ground-truthinformation to update parameters of the pixel angle estimation module517.

After being trained, the pixel angle estimation module 517 can computeor estimate pixel angles and/or lighting conditions for pixels of aportion of an image corresponding to a fashion item in real time. Thepixel angle estimation module 517 can continuously or periodicallyupdate the pixel angles and/or lighting conditions as the fashion itemmoves around a video. The pixel angles and/or lighting conditionsprovided by the pixel angle estimation module 517 are used by the AReffect module 519 to update the pixel values of the portion of the imagecorresponding to the fashion item to represent reflection and/orabsorption of light from different angles and for different materials.In some cases, the AR effect module 519 updates the pixel valuesindependently from movement information provided by the 3D body trackingmodule 513.

A user of the AR/VR application may be presented with an option toselect an AR application or experience to control display of AR elementson a shirt worn by the user, such as to change a material of the shirtor fashion item or re-focus light from a different direction (e.g., toapply artificial or AR light to the fashion item). In response toreceiving a user selection of the option, a camera (e.g., front-facingor rear-facing camera) is activated to begin capturing an image or videoof the user wearing a shirt (or upper garment or fashion item). Theimage or video depicting the user wearing the shirt (or upper garment orfashion item) is provided to the AR effect module 519 to apply one ormore AR elements to the shirt or to change the pixel values of the shirtto represent a different material or light being focused from adifferent direction. The AR effect module 519 selects between variousapplications/modifications of AR elements displayed on the shirt (orupper garment or fashion item) worn by the user, such as based ongestures or movement of the user detected by the 3D body tracking module513 and/or whole-body segmentation module 515. FIGS. 7 and 8 showillustrative outputs of one or more of the visual effects that can beselected and applied by the AR effect module 519.

For example, the AR effect module 519 can determine that light is beingfocused on the fashion item (shirt or upper garment) from a firstdirection based on the estimated angle of each pixel. In response, theAR effect module 519 can modify pixel values of the portion of thefashion item to re-focus the light on the fashion item from a seconddirection based on the estimated angle of each pixel. Specifically, theAR effect module 519 can determine that a first pixel in the portion ofthe image is pointing towards a given direction relative to the cameraor surface normal of the camera. In such cases, the AR effect module 519can modify the first pixel to render a reflection of the re-focusedlight from the second direction towards the given direction.

As another example, the AR effect module 519 can select or receive aselection of an AR material for the fashion item and can replace amaterial of the fashion item with the AR material based on the estimatedangle of each pixel. The AR effect module 519 can display a list ofdifferent AR materials and can receive a selection of the AR materialfrom the list. The selection can be made verbally using speech inputfrom the user or by receiving touch input from the user touching aparticular one of the AR materials in the list.

The AR effect module 519 can determine a light reflection or absorptionproperty of the AR material. In response, the AR effect module 519 can,for each pixel in the portion of the image, compute a new pixel valuebased on the light reflection or absorption property of the AR materialand the estimated angle of the pixel. In this way, the AR effect module519 can apply different AR shades to the fashion item depicted in theimage or video for each different type of the AR material that isselected. In one example, the fashion item worn by the person depictedin the image or video can be made of a cloth or cloth-like material andthe AR effect module 519 can replace the cloth or cloth-like materialwith an AR material, such as gold, mirror, metal, water, or slime. Insuch cases, the light reflection and/or absorption properties of thefashion item worn by the person depicted in the image or video ischanged to correspond to the light reflection and/or absorptionproperties of the AR material. To perform such AR changes, the AR effectmodule 519 can modify each pixel value of the portion of the image thatcorresponds to the fashion item based on the estimated angle of thecorresponding pixel relative to the camera. The value of the pixel ofthe portion of the fashion item represents the direction at which lightis reflected or absorbed by that portion of the fashion item made of theAR material and differs from the direction at which light is reflectedor absorbed that portion of the fashion item made by the cloth orcloth-like material.

The image modification module 518 can adjust the image captured by thecamera based on the game-based AR effect selected by the AR effectmodule 519. The image modification module 518 adjusts the way in whichthe garment(s) worn by the user is/are presented in an image or video,such as by changing the color or occlusion pattern of the garment wornby the user based on the garment segmentation and applying one or moreAR elements to the fashion item worn by the user depicted in the imageor video. Image display module 520 combines the adjustments made by theimage modification module 518 into the received monocular image or videodepicting the user's body. The image or video is provided by the imagedisplay module 520 to the client device 102 and can then be sent toanother user or stored for later access and display.

In some examples, the image modification module 518 receives 3D bodytracking information representing the 3D positions of the user depictedin the image from the 3D body tracking module 513. The 3D body trackingmodule 513 generates the 3D body tracking information by processing theimage 501 using additional machine learning techniques. The imagemodification module 518 can also receive a whole-body segmentationrepresenting which pixels in the image correspond to the whole body ofthe user from another machine learning technique. The whole-bodysegmentation can be received from the whole-body segmentation module515. The whole-body segmentation module 515 generates the whole-bodysegmentation by processing the image 501 using a machine learningtechnique.

The image modification module 518 can control the display of virtual orAR elements based on the garment segmentation provided by the uppergarment segmentation module 514 and based on the 3D body trackingpositions of the user and the whole-body segmentation of the user.

In one example, as shown in FIG. 7 , the AR effect module 519 can applyone or more AR effects 730 to a shirt 710 worn by a user 720 depicted inan image 700 captured by a client device 102. The one or more AR effects730 can include an AR game board, an AR gaming controller forcontrolling a gaming application interface, an AR ball game, an ARcapture game, an AR flying saucer game, or any other type of AR gamingexperience that can be displayed on a fashion item of a user andinteracted by gestures performed by the user. Other types of AR effects730 can include AR music related AR elements (discussed above), ARavatars, AR voice transcriptions, AR elements that are based on voiceexpressions of the use 720, and the like. Other types of AR effects 730can represent different material properties of the shirt 710 (e.g.,replacing the shirt from being made of cloth to being made of gold,water or slime). Other types of AR effects 730 can represent differentlighting conditions on the shirt 710, such as refocusing light fromcoming from the top using artificial or AR light coming from the bottomof the image. In such cases, the light reflection and/or absorption onthe shirt 710 changes and is represented by modifying the pixel valuesof the shirt 710, such as based on their respective pixel angle relativeto the camera used to capture the image of the shirt 710.

As another example shown in FIG. 8 , the AR effect module 519 cangenerate replace material properties of the shirt 810 (or fashion item)worn by the person or user 826 depicted in the image 800 or video. TheAR effect module 519 can select a new material property for the fashionitem associated with an AR experience. In some cases, the AR effectmodule 519 can receive a request to replace the material of a firstfashion item (e.g., the shirt 810) with a first material type (e.g.,gold) and can receive a request to replace the material of a secondfashion item (e.g., pants or certain portion of the shirt 810) with asecond material type (e.g., water). Pixel values of the fashion item orfashion items are modified to represent reflections and/or absorptionsof light based on the pixel angles of each fashion item and based on theproperties of the selected material type (e.g., water can be moreabsorptive of light than gold).

The pixel values are further modified based on tracking movement of theperson depicted in the image 800 or video. For example, the shirt 810can be moved around in the video based on movement of the user 826 andchanges to the light reflection and/or absorption of the pixels isupdated based on newly computed pixel angle estimations provided by thepixel angle estimation module 517. The pixel values are modified torepresent the new angles that result based on movement of the user 826and to preserve the lighting conditions resulting from the directionfrom which real or AR light is focused on the fashion item and/or the ARmaterial properties of the corresponding fashion item. The AR effectmodule 519 can rotate the AR elements based on rotations of the user 826to represent differences in lighting conditions (e.g., differences inlight reflections and/or absorptions) on the fashion item as the pixelangles change when the user 826 rotates. This makes it appear as thoughthe AR lighting and/or AR materials of the shirt 810 are part of thereal-world world.

FIG. 9 is a flowchart of a process 900 performed by the AR fashioncontrol system 224, in accordance with some example examples. Althoughthe flowchart can describe the operations as a sequential process, manyof the operations can be performed in parallel or concurrently. Inaddition, the order of the operations may be re-arranged. A process isterminated when its operations are completed. A process may correspondto a method, a procedure, and the like. The steps of methods may beperformed in whole or in part, may be performed in conjunction with someor all of the steps in other methods, and may be performed by any numberof different systems or any portion thereof, such as a processorincluded in any of the systems.

At operation 901, the AR fashion control system 224 (e.g., a clientdevice 102 or a server) receives an image that includes a depiction of aperson wearing a fashion item, as discussed above.

At operation 902, the AR fashion control system 224 generates asegmentation of the fashion item worn by the person depicted in theimage, as discussed above.

At operation 903, the AR fashion control system 224 extracts a portionof the image corresponding to the segmentation of the fashion item, asdiscussed above.

At operation 904, the AR fashion control system 224 estimates an angleof each pixel in the portion of the image relative to a camera used tocapture the image, as discussed above.

At operation 905, the AR fashion control system 224 applies one or moreAR elements to the fashion item in the image based on the estimatedangle of each pixel in the portion of the image relative to the cameraused to capture the image, as discussed above.

Machine Architecture

FIG. 10 is a diagrammatic representation of the machine 1000 withinwhich instructions 1008 (e.g., software, a program, an application, anapplet, an app, or other executable code) for causing the machine 1000to perform any one or more of the methodologies discussed herein may beexecuted. For example, the instructions 1008 may cause the machine 1000to execute any one or more of the methods described herein. Theinstructions 1008 transform the general, non-programmed machine 1000into a particular machine 1000 programmed to carry out the described andillustrated functions in the manner described. The machine 1000 mayoperate as a standalone device or may be coupled (e.g., networked) toother machines. In a networked deployment, the machine 1000 may operatein the capacity of a server machine or a client machine in aserver-client network environment, or as a peer machine in apeer-to-peer (or distributed) network environment. The machine 1000 maycomprise, but not be limited to, a server computer, a client computer, apersonal computer (PC), a tablet computer, a laptop computer, a netbook,a set-top box (STB), a personal digital assistant (PDA), anentertainment media system, a cellular telephone, a smartphone, a mobiledevice, a wearable device (e.g., a smartwatch), a smart home device(e.g., a smart appliance), other smart devices, a web appliance, anetwork router, a network switch, a network bridge, or any machinecapable of executing the instructions 1008, sequentially or otherwise,that specify actions to be taken by the machine 1000. Further, whileonly a single machine 1000 is illustrated, the term “machine” shall alsobe taken to include a collection of machines that individually orjointly execute the instructions 1008 to perform any one or more of themethodologies discussed herein. The machine 1000, for example, maycomprise the client device 102 or any one of a number of server devicesforming part of the messaging server system 108. In some examples, themachine 1000 may also comprise both client and server systems, withcertain operations of a particular method or algorithm being performedon the server-side and with certain operations of the particular methodor algorithm being performed on the client-side.

The machine 1000 may include processors 1002, memory 1004, andinput/output (I/O) components 1038, which may be configured tocommunicate with each other via a bus 1040. In an example, theprocessors 1002 (e.g., a Central Processing Unit (CPU), a ReducedInstruction Set Computing (RISC) Processor, a Complex Instruction SetComputing (CISC) Processor, a Graphics Processing Unit (GPU), a DigitalSignal Processor (DSP), an Application Specific Integrated Circuit(ASIC), a Radio-Frequency Integrated Circuit (RFIC), another processor,or any suitable combination thereof) may include, for example, aprocessor 1006 and a processor 1010 that execute the instructions 1008.The term “processor” is intended to include multi-core processors thatmay comprise two or more independent processors (sometimes referred toas “cores”) that may execute instructions contemporaneously. AlthoughFIG. 10 shows multiple processors 1002, the machine 1000 may include asingle processor with a single-core, a single processor with multiplecores (e.g., a multi-core processor), multiple processors with a singlecore, multiple processors with multiples cores, or any combinationthereof.

The memory 1004 includes a main memory 1012, a static memory 1014, and astorage unit 1016, all accessible to the processors 1002 via the bus1040. The main memory 1004, the static memory 1014, and the storage unit1016 store the instructions 1008 embodying any one or more of themethodologies or functions described herein. The instructions 1008 mayalso reside, completely or partially, within the main memory 1012,within the static memory 1014, within machine-readable medium 1018within the storage unit 1016, within at least one of the processors 1002(e.g., within the processor's cache memory), or any suitable combinationthereof, during execution thereof by the machine 1000.

The I/O components 1038 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 1038 that are included in a particular machine will depend onthe type of machine. For example, portable machines such as mobilephones may include a touch input device or other such input mechanisms,while a headless server machine will likely not include such a touchinput device. It will be appreciated that the I/O components 1038 mayinclude many other components that are not shown in FIG. 10 . In variousexamples, the I/O components 1038 may include user output components1024 and user input components 1026. The user output components 1024 mayinclude visual components (e.g., a display such as a plasma displaypanel (PDP), a light-emitting diode (LED) display, a liquid crystaldisplay (LCD), a projector, or a cathode ray tube (CRT)), acousticcomponents (e.g., speakers), haptic components (e.g., a vibratory motor,resistance mechanisms), other signal generators, and so forth. The userinput components 1026 may include alphanumeric input components (e.g., akeyboard, a touch screen configured to receive alphanumeric input, aphoto-optical keyboard, or other alphanumeric input components),point-based input components (e.g., a mouse, a touchpad, a trackball, ajoystick, a motion sensor, or another pointing instrument), tactileinput components (e.g., a physical button, a touch screen that provideslocation and force of touches or touch gestures, or other tactile inputcomponents), audio input components (e.g., a microphone), and the like.

In further examples, the I/O components 1038 may include biometriccomponents 1028, motion components 1030, environmental components 1032,or position components 1034, among a wide array of other components. Forexample, the biometric components 1028 include components to detectexpressions (e.g., hand expressions, facial expressions, vocalexpressions, body gestures, or eye-tracking), measure biosignals (e.g.,blood pressure, heart rate, body temperature, perspiration, or brainwaves), identify a person (e.g., voice identification, retinalidentification, facial identification, fingerprint identification, orelectroencephalogram-based identification), and the like. The motioncomponents 1030 include acceleration sensor components (e.g.,accelerometer), gravitation sensor components, and rotation sensorcomponents (e.g., gyroscope).

The environmental components 1032 include, for example, one or morecameras (with still image/photograph and video capabilities),illumination sensor components (e.g., photometer), temperature sensorcomponents (e.g., one or more thermometers that detect ambienttemperature), humidity sensor components, pressure sensor components(e.g., barometer), acoustic sensor components (e.g., one or moremicrophones that detect background noise), proximity sensor components(e.g., infrared sensors that detect nearby objects), gas sensors (e.g.,gas detection sensors to detection concentrations of hazardous gases forsafety or to measure pollutants in the atmosphere), or other componentsthat may provide indications, measurements, or signals corresponding toa surrounding physical environment.

With respect to cameras, the client device 102 may have a camera systemcomprising, for example, front cameras on a front surface of the clientdevice 102 and rear cameras on a rear surface of the client device 102.The front cameras may, for example, be used to capture still images andvideo of a user of the client device 102 (e.g., “selfies”), which maythen be augmented with augmentation data (e.g., filters) describedabove. The rear cameras may, for example, be used to capture stillimages and videos in a more traditional camera mode, with these imagessimilarly being augmented with augmentation data. In addition to frontand rear cameras, the client device 102 may also include a 360° camerafor capturing 360° photographs and videos.

Further, the camera system of a client device 102 may include dual rearcameras (e.g., a primary camera as well as a depth-sensing camera), oreven triple, quad, or penta rear camera configurations on the front andrear sides of the client device 102. These multiple cameras systems mayinclude a wide camera, an ultra-wide camera, a telephoto camera, a macrocamera, and a depth sensor, for example.

The position components 1034 include location sensor components (e.g., aGPS receiver component), altitude sensor components (e.g., altimeters orbarometers that detect air pressure from which altitude may be derived),orientation sensor components (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 1038 further include communication components 1036operable to couple the machine 1000 to a network 1020 or devices 1022via respective coupling or connections. For example, the communicationcomponents 1036 may include a network interface component or anothersuitable device to interface with the network 1020. In further examples,the communication components 1036 may include wired communicationcomponents, wireless communication components, cellular communicationcomponents, Near Field Communication (NFC) components, Bluetooth®components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and othercommunication components to provide communication via other modalities.The devices 1022 may be another machine or any of a wide variety ofperipheral devices (e.g., a peripheral device coupled via a USB).

Moreover, the communication components 1036 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 1036 may include Radio Frequency Identification(RFID) tag reader components, NFC smart tag detection components,optical reader components (e.g., an optical sensor to detectone-dimensional bar codes such as Universal Product Code (UPC) bar code,multi-dimensional bar codes such as Quick Response (QR) code, Azteccode, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2Dbar code, and other optical codes), or acoustic detection components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication components1036, such as location via Internet Protocol (IP) geolocation, locationvia Wi-Fi® signal triangulation, location via detecting an NFC beaconsignal that may indicate a particular location, and so forth.

The various memories (e.g., main memory 1012, static memory 1014, andmemory of the processors 1002) and storage unit 1016 may store one ormore sets of instructions and data structures (e.g., software) embodyingor used by any one or more of the methodologies or functions describedherein. These instructions (e.g., the instructions 1008), when executedby processors 1002, cause various operations to implement the disclosedexamples.

The instructions 1008 may be transmitted or received over the network1020, using a transmission medium, via a network interface device (e.g.,a network interface component included in the communication components1036) and using any one of several well-known transfer protocols (e.g.,HTTP). Similarly, the instructions 1008 may be transmitted or receivedusing a transmission medium via a coupling (e.g., a peer-to-peercoupling) to the devices 1022.

Software Architecture

FIG. 11 is a block diagram 1100 illustrating a software architecture1104, which can be installed on any one or more of the devices describedherein. The software architecture 1104 is supported by hardware such asa machine 1102 that includes processors 1120, memory 1126, and I/Ocomponents 1138. In this example, the software architecture 1104 can beconceptualized as a stack of layers, where each layer provides aparticular functionality. The software architecture 1104 includes layerssuch as an operating system 1112, libraries 1110, frameworks 1108, andapplications 1106. Operationally, the applications 1106 invoke API calls1150 through the software stack and receive messages 1152 in response tothe API calls 1150.

The operating system 1112 manages hardware resources and provides commonservices. The operating system 1112 includes, for example, a kernel1114, services 1116, and drivers 1122. The kernel 1114 acts as anabstraction layer between the hardware and the other software layers.For example, the kernel 1114 provides memory management, processormanagement (e.g., scheduling), component management, networking, andsecurity settings, among other functionalities. The services 1116 canprovide other common services for the other software layers. The drivers1122 are responsible for controlling or interfacing with the underlyinghardware. For instance, the drivers 1122 can include display drivers,camera drivers, BLUETOOTH® or BLUETOOTH® Low Energy drivers, flashmemory drivers, serial communication drivers (e.g., USB drivers), WI-FI®drivers, audio drivers, power management drivers, and so forth.

The libraries 1110 provide a common low-level infrastructure used byapplications 1106. The libraries 1110 can include system libraries 1118(e.g., C standard library) that provide functions such as memoryallocation functions, string manipulation functions, mathematicfunctions, and the like. In addition, the libraries 1110 can include APIlibraries 1124 such as media libraries (e.g., libraries to supportpresentation and manipulation of various media formats such as MovingPicture Experts Group-4 (MPEG4), Advanced Video Coding (H.264 or AVC),Moving Picture Experts Group Layer-3 (MP3), Advanced Audio Coding (AAC),Adaptive Multi-Rate (AMR) audio codec, Joint Photographic Experts Group(JPEG or JPG), or Portable Network Graphics (PNG)), graphics libraries(e.g., an OpenGL framework used to render in 2D and 3D in a graphiccontent on a display), database libraries (e.g., SQLite to providevarious relational database functions), web libraries (e.g., WebKit toprovide web browsing functionality), and the like. The libraries 1110can also include a wide variety of other libraries 1128 to provide manyother APIs to the applications 1106.

The frameworks 1108 provide a common high-level infrastructure that isused by the applications 1106. For example, the frameworks 1108 providevarious graphical user interface functions, high-level resourcemanagement, and high-level location services. The frameworks 1108 canprovide a broad spectrum of other APIs that can be used by theapplications 1106, some of which may be specific to a particularoperating system or platform.

In an example, the applications 1106 may include a home application1136, a contacts application 1130, a browser application 1132, a bookreader application 1134, a location application 1142, a mediaapplication 1144, a messaging application 1146, a game application 1148,and a broad assortment of other applications such as an externalapplication 1140. The applications 1106 are programs that executefunctions defined in the programs. Various programming languages can beemployed to create one or more of the applications 1106, structured in avariety of manners, such as object-oriented programming languages (e.g.,Objective-C, Java, or C++) or procedural programming languages (e.g., Cor assembly language). In a specific example, the external application1140 (e.g., an application developed using the ANDROID™ or IOS™ SDK byan entity other than the vendor of the particular platform) may bemobile software running on a mobile operating system such as IOS™,ANDROID™, WINDOWS® Phone, or another mobile operating system. In thisexample, the external application 1140 can invoke the API calls 1150provided by the operating system 1112 to facilitate functionalitydescribed herein.

Glossary

“Carrier signal” refers to any intangible medium that is capable ofstoring, encoding, or carrying instructions for execution by themachine, and includes digital or analog communications signals or otherintangible media to facilitate communication of such instructions.Instructions may be transmitted or received over a network using atransmission medium via a network interface device.

“Client device” refers to any machine that interfaces to acommunications network to obtain resources from one or more serversystems or other client devices. A client device may be, but is notlimited to, a mobile phone, desktop computer, laptop, portable digitalassistant (PDA), smartphone, tablet, ultrabook, netbook, laptop,multi-processor system, microprocessor-based or programmable consumerelectronics, game console, set-top box, or any other communicationdevice that a user may use to access a network.

“Communication network” refers to one or more portions of a network thatmay be an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local area network (LAN), a wireless LAN (WLAN), a widearea network (WAN), a wireless WAN (WWAN), a metropolitan area network(MAN), the Internet, a portion of the Internet, a portion of the PublicSwitched Telephone Network (PSTN), a plain old telephone service (POTS)network, a cellular telephone network, a wireless network, a Wi-Fi®network, another type of network, or a combination of two or more suchnetworks. For example, a network or a portion of a network may include awireless or cellular network and the coupling may be a Code DivisionMultiple Access (CDMA) connection, a Global System for Mobilecommunications (GSM) connection, or other types of cellular or wirelesscoupling. In this example, the coupling may implement any of a varietyof types of data transfer technology, such as Single Carrier RadioTransmission Technology (1×RTT), Evolution-Data Optimized (EVDO)technology, General Packet Radio Service (GPRS) technology, EnhancedData rates for GSM Evolution (EDGE) technology, third GenerationPartnership Project (3GPP) including 3G, fourth generation wireless (4G)networks, Universal Mobile Telecommunications System (UNITS), High SpeedPacket Access (HSPA), Worldwide Interoperability for Microwave Access(WiMAX), Long Term Evolution (LTE) standard, others defined by variousstandard-setting organizations, other long-range protocols, or otherdata transfer technology.

“Component” refers to a device, physical entity, or logic havingboundaries defined by function or subroutine calls, branch points, APIs,or other technologies that provide for the partitioning ormodularization of particular processing or control functions. Componentsmay be combined via their interfaces with other components to carry outa machine process. A component may be a packaged functional hardwareunit designed for use with other components and a part of a program thatusually performs a particular function of related functions.

Components may constitute either software components (e.g., codeembodied on a machine-readable medium) or hardware components. A“hardware component” is a tangible unit capable of performing certainoperations and may be configured or arranged in a certain physicalmanner. In various examples, one or more computer systems (e.g., astandalone computer system, a client computer system, or a servercomputer system) or one or more hardware components of a computer system(e.g., a processor or a group of processors) may be configured bysoftware (e.g., an application or application portion) as a hardwarecomponent that operates to perform certain operations as describedherein.

A hardware component may also be implemented mechanically,electronically, or any suitable combination thereof. For example, ahardware component may include dedicated circuitry or logic that ispermanently configured to perform certain operations. A hardwarecomponent may be a special-purpose processor, such as afield-programmable gate array (FPGA) or an ASIC. A hardware componentmay also include programmable logic or circuitry that is temporarilyconfigured by software to perform certain operations. For example, ahardware component may include software executed by a general-purposeprocessor or other programmable processor. Once configured by suchsoftware, hardware components become specific machines (or specificcomponents of a machine) uniquely tailored to perform the configuredfunctions and are no longer general-purpose processors. It will beappreciated that the decision to implement a hardware componentmechanically, in dedicated and permanently configured circuitry, or intemporarily configured circuitry (e.g., configured by software), may bedriven by cost and time considerations. Accordingly, the phrase“hardware component” (or “hardware-implemented component”) should beunderstood to encompass a tangible entity, be that an entity that isphysically constructed, permanently configured (e.g., hardwired), ortemporarily configured (e.g., programmed) to operate in a certain manneror to perform certain operations described herein.

Considering examples in which hardware components are temporarilyconfigured (e.g., programmed), each of the hardware components need notbe configured or instantiated at any one instance in time. For example,where a hardware component comprises a general-purpose processorconfigured by software to become a special-purpose processor, thegeneral-purpose processor may be configured as respectively differentspecial-purpose processors (e.g., comprising different hardwarecomponents) at different times. Software accordingly configures aparticular processor or processors, for example, to constitute aparticular hardware component at one instance of time and to constitutea different hardware component at a different instance of time.

Hardware components can provide information to, and receive informationfrom, other hardware components. Accordingly, the described hardwarecomponents may be regarded as being communicatively coupled. Wheremultiple hardware components exist contemporaneously, communications maybe achieved through signal transmission (e.g., over appropriate circuitsand buses) between or among two or more of the hardware components. Inexamples in which multiple hardware components are configured orinstantiated at different times, communications between such hardwarecomponents may be achieved, for example, through the storage andretrieval of information in memory structures to which the multiplehardware components have access. For example, one hardware component mayperform an operation and store the output of that operation in a memorydevice to which it is communicatively coupled. A further hardwarecomponent may then, at a later time, access the memory device toretrieve and process the stored output. Hardware components may alsoinitiate communications with input or output devices, and can operate ona resource (e.g., a collection of information).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implementedcomponents that operate to perform one or more operations or functionsdescribed herein. As used herein, “processor-implemented component”refers to a hardware component implemented using one or more processors.Similarly, the methods described herein may be at least partiallyprocessor-implemented, with a particular processor or processors beingan example of hardware. For example, at least some of the operations ofa method may be performed by one or more processors 1002 orprocessor-implemented components. Moreover, the one or more processorsmay also operate to support performance of the relevant operations in a“cloud computing” environment or as a “software as a service” (SaaS).For example, at least some of the operations may be performed by a groupof computers (as examples of machines including processors), with theseoperations being accessible via a network (e.g., the Internet) and viaone or more appropriate interfaces (e.g., an API). The performance ofcertain of the operations may be distributed among the processors, notonly residing within a single machine, but deployed across a number ofmachines. In some examples, the processors or processor-implementedcomponents may be located in a single geographic location (e.g., withina home environment, an office environment, or a server farm). In otherexamples, the processors or processor-implemented components may bedistributed across a number of geographic locations.

“Computer-readable storage medium” refers to both machine-storage mediaand transmission media. Thus, the terms include both storagedevices/media and carrier waves/modulated data signals. The terms“machine-readable medium,” “computer-readable medium,” and“device-readable medium” mean the same thing and may be usedinterchangeably in this disclosure.

“Ephemeral message” refers to a message that is accessible for atime-limited duration. An ephemeral message may be a text, an image, avideo, and the like. The access time for the ephemeral message may beset by the message sender. Alternatively, the access time may be adefault setting or a setting specified by the recipient. Regardless ofthe setting technique, the message is transitory.

“Machine storage medium” refers to a single or multiple storage devicesand media (e.g., a centralized or distributed database, and associatedcaches and servers) that store executable instructions, routines, anddata. The term shall accordingly be taken to include, but not be limitedto, solid-state memories, and optical and magnetic media, includingmemory internal or external to processors. Specific examples ofmachine-storage media, computer-storage media and device-storage mediainclude non-volatile memory, including by way of example semiconductormemory devices, e.g., erasable programmable read-only memory (EPROM),electrically erasable programmable read-only memory (EEPROM), FPGA, andflash memory devices; magnetic disks such as internal hard disks andremovable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks Theterms “machine-storage medium,” “device-storage medium,” and“computer-storage medium” mean the same thing and may be usedinterchangeably in this disclosure. The terms “machine-storage media,”“computer-storage media,” and “device-storage media” specificallyexclude carrier waves, modulated data signals, and other such media, atleast some of which are covered under the term “signal medium.”

“Non-transitory computer-readable storage medium” refers to a tangiblemedium that is capable of storing, encoding, or carrying theinstructions for execution by a machine.

“Signal medium” refers to any intangible medium that is capable ofstoring, encoding, or carrying the instructions for execution by amachine and includes digital or analog communications signals or otherintangible media to facilitate communication of software or data. Theterm “signal medium” shall be taken to include any form of a modulateddata signal, carrier wave, and so forth. The term “modulated datasignal” means a signal that has one or more of its characteristics setor changed in such a matter as to encode information in the signal. Theterms “transmission medium” and “signal medium” mean the same thing andmay be used interchangeably in this disclosure.

Changes and modifications may be made to the disclosed examples withoutdeparting from the scope of the present disclosure. These and otherchanges or modifications are intended to be included within the scope ofthe present disclosure, as expressed in the following claims.

What is claimed is:
 1. A method comprising: receiving, by one or moreprocessors of a client device, an image that includes a depiction of aperson wearing a fashion item; generating, by the one or moreprocessors, a segmentation of the fashion item worn by the persondepicted in the image; extracting a portion of the image correspondingto the segmentation of the fashion item; estimating an angle of eachpixel in the portion of the image relative to a camera used to capturethe image; and applying one or more augmented reality (AR) elements tothe fashion item in the image based on the estimated angle of each pixelin the portion of the image relative to the camera used to capture theimage.
 2. The method of claim 1, wherein applying the one or more ARelements comprises: determining that light is being focused on thefashion item from a first direction based on the estimated angle of eachpixel; and modifying pixel values of the portion of the fashion item tore-focus the light on the fashion item from a second direction based onthe estimated angle of each pixel.
 3. The method of claim 2, furthercomprising: determining that a first pixel in the portion of the imageis pointing towards a given direction relative to the camera; andmodifying the first pixel to render a reflection of the re-focused lightfrom the second direction towards the given direction.
 4. The method ofclaim 2, wherein the light in the image is being focused from a top ofthe image towards a bottom of the image, and wherein the pixel valuesare modified to re-focus the light from the bottom towards the top. 5.The method of claim 1, wherein applying the one or more AR elementscomprises: selecting an AR material for the fashion item; and replacinga material of the fashion item with the AR material based on theestimated angle of each pixel.
 6. The method of claim 5, furthercomprising: determining a light reflection or absorption property of theAR material; and for each pixel in the portion of the image, computing anew pixel value based on the light reflection or absorption property ofthe AR material and the estimated angle of the pixel.
 7. The method ofclaim 5, further comprising applying different AR shades to the fashionitem for each different type of the AR material that is selected.
 8. Themethod of claim 5, further comprising: displaying a list of different ARmaterials; and receiving a selection of the AR material from the list.9. The method of claim 5, wherein the AR material comprises gold,mirror, water, or slime.
 10. The method of claim 1, wherein applying theone or more AR elements comprises applying artificial light to thefashion item based on the estimated angle of each pixel.
 11. The methodof claim 1, further comprising: displaying the one or more AR elementson a first portion of the fashion item in a first frame of a video,wherein the person is positioned at a first location in the first frame;determining that the person has moved from the first location to asecond location in a second frame of the video, the first portion of thefashion item being moved to a new location in the second frame of thevideo; and updating a display position of the one or more AR elements inthe second frame to maintain the display of the one or more AR elementson the first portion of the fashion item.
 12. The method of claim 1,further comprising: determining that the person depicted in the imagehas been rotated by a specified amount; and rotating the one or more ARelements by the specified amount in response to determining that theperson has been rotated by the specified amount.
 13. The method of claim1, wherein the estimated angle of each pixel is computed relative to asurface normal of the camera.
 14. The method of claim 1, whereinestimating the angle of each pixel in the portion of the image relativeto the camera used to capture the image comprises: applying a neuralnetwork to the portion of the image, the neural network being trained toestablish a relationship between image portions depicting differentorientations of fashion items and pixel directions of the fashion itemsrelative to surface normals of cameras used to capture the imageportions.
 15. The method of claim 1, wherein estimating the angle ofeach pixel in the portion of the image relative to the camera used tocapture the image comprises: applying a neural network to the portion ofthe image, the neural network being trained to establish a relationshipbetween image portions depicting reflections of light from differentdirections on fashion items and pixel directions of the fashion itemsrelative to surface normals of cameras used to capture the imageportions.
 16. The method of claim 1, further comprising: receiving arequest to apply AR light to the fashion item from a new direction; andtraining a neural network to estimate lighting conditions for each pixelin the portion when the AR light is applied from the new direction. 17.The method of claim 16, wherein the neural network is trained toestablish a relationship between image portions depicting focusing oflight on fashion items from different directions and lighting conditionsof pixels of the fashion items.
 18. A system comprising: a processor ofa client device; and a memory component having instructions storedthereon that, when executed by the processor, cause the processor toperform operations comprising: receiving an image that includes adepiction of a person wearing a fashion item; generating a segmentationof the fashion item worn by the person depicted in the image; extractinga portion of the image corresponding to the segmentation of the fashionitem; estimating an angle of each pixel in the portion of the imagerelative to a camera used to capture the image; and applying one or moreaugmented reality elements to the fashion item in the image based on theestimated angle of each pixel in the portion of the image relative tothe camera used to capture the image.
 19. The system of claim 18,wherein applying the one or more augmented reality elements comprises:determining that light is being focused on the fashion item from a firstdirection based on the estimated angle of each pixel; and modifyingpixel values of the portion of the fashion item to re-focus the light onthe fashion item from a second direction based on the estimated angle ofeach pixel.
 20. A non-transitory computer-readable storage medium havingstored thereon instructions that, when executed by a processor of aclient device, cause the processor to perform operations comprising:receiving an image that includes a depiction of a person wearing afashion item; generating a segmentation of the fashion item worn by theperson depicted in the image; extracting a portion of the imagecorresponding to the segmentation of the fashion item; estimating anangle of each pixel in the portion of the image relative to a cameraused to capture the image; and applying one or more augmented realityelements to the fashion item in the image based on the estimated angleof each pixel in the portion of the image relative to the camera used tocapture the image.